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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 OSTI2021

Evaluation of several relevant fractionation processes as possible explanation for radioxenon isotopic activity ratios in samples taken near underground nuclear explosions in shafts and tunnels

Gas samples taken from two historic underground nuclear tests done in 1989 at the Nevada National Security Site (NNSS), formerly the Nevada Test Site (NTS), were examined to determine how xenon isotopes fractionate because of early-time cavity processes, transport through the rock, or dispersal through tunnels. Xenon isotopes are currently being used to distinguish civilian sources of xenon in the atmosphere from sources associated with underground nuclear explosions (UNEs). The two nuclear tests included (1) BARNWELL, a test conducted in a vertical shaft approximately 600 m below ground surface at Pahute Mesa, and (2) DISKO ELM, a horizontal line-of-sight test done in P-tunnel approximately 261 m below the surface of Aqueduct Mesa. Numerical flow and transport models developed for the two sites had mixed success when attempting to match the observed xenon isotope ratios. At the BARNWELL site, the simulated xenon isotope ratios were consistent with measurements from the chimney and ground surface and appeared to have been affected primarily by fractionation during subsurface transport. At the DISKO ELM site, samples taken from two elevations in the chimney failed to show the degree of fractionation predicted by the models during transport and did not show evidence for significant fractionation due to early-time condensation of refractory xenon-precursor radionuclides into the melt glass. Gas samples taken from the adjacent tunnels in the days following the test showed mixed evidence for early-time separation of xenon isotopes from their iodine precursors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Grain boundary relaxation in doped nano-grained aluminum

Here, simulation studies are done to understand the role of dopants that segregate preferentially to grain boundaries on the stability of nanocrystalline aluminum. A dopant design framework based on thermodynamic principles, is used to identify the specific dopant type with the highest potential to segregate to grain boundaries in nanocrystalline aluminum. Various elements are evaluated as potential dopants and magnesium is identified to have the highest tendency to segregate to grain boundaries and release the excess free energyleading to the relaxation of the grain boundaries. A systematic combination of atomic structure analysis is then done to correlate grain boundary relaxation and the mechanical response of the magnesium-doped nanocrystalline aluminum at ambient temperature. The atomistic simulations reveal that the preferential partitioning of magnesium dopants to the grain boundaries reduces the excess volume within this region which stabilizes the nanostructure. At low contents, the magnesium dopants are observed initially partition to the grain boundaries, but once saturation of the grain boundaries is reached, excess dopants are accommodated in the crystalline interiors. It is found that the addition of the magnesium dopants even in the dilute limit, enhances the strength of the nanocrystalline aluminum. The formation of large, disordered GBs in doped nanocrystalline aluminum under tensile load allowed it to accommodate the deformation and prohibit crack growth.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Combined Rotating Disk Slurry Electrodeposition–Spontaneous Galvanic Displacement for Pt-M (M = Co, Ni, and Cu) Catalyst Synthesis for the Oxygen Reduction Reaction in Alkaline Media

Here, platinum-modified first-row transition-metal catalyst (M = Ni, Co, and Cu) nanoparticles on a carbon black Vulcan XC-72R (CBV) support were synthesized using rotating disk slurry electrodeposition (RoDSE) followed by spontaneous galvanic displacement (SGD) reactions. These RoDSE–SGD catalysts were evaluated for the oxygen reduction reaction (ORR) in 0.1 M KOH using rotating disk electrode techniques. The Ni and Co RoDSEs were done by using an electrochemical applied potential of -0.75 V versus the RHE and for Cu, -0.80 V versus the RHE using a CBV slurry solution containing 0.1 M KClO 4 . These metal nanoparticles on CBV (M/CBV) catalysts were modified with a Pt precursor via a spontaneous galvanic displacement (SGD) reaction, producing a Pt-M/CBV material to catalyze the ORR in an alkaline medium. High-resolution scanning transmission electron microscopy (HR-STEM) analysis indicates that the PtM/CBV samples include M clusters and Pt single atoms. The ORR characterization measurements were done under a controlled temperature (25.0 °C) and with a mass loading of 100 μg/cm 2 on a glassy carbon (GC) rotating disk electrode at 1600 rpm. The PtCo/CBV showed the highest ORR mass activity of 0.741 A/mgPt at 0.90 V versus the reversible hydrogen electrode (RHE) compared with commercial Pt/CBV. The M/CBV RoDSE catalysts were also tested for the oxygen evolution reaction (OER), and Ni/CBV provided the lowest overpotential of 450 mV at 10 mA/cm 2 disk in 0.1 M KOH.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Method of Distributions for Two‐Phase Flow in Heterogeneous Porous Media

Abstract Multiscale heterogeneity and insufficient characterization data for a specific subsurface formation of interest render predictions of multi‐phase fluid flow in geologic formations highly uncertain. Quantification of the uncertainty propagation from the geomodel to the fluid‐flow response is typically done within a probabilistic framework. This task is computationally demanding due to, for example, the slow convergence of Monte Carlo simulations (MCS), especially when computing the tails of a distribution that are necessary for risk assessment and decision‐making under uncertainty. The frozen streamlines method (FROST) accelerates probabilistic predictions of immiscible two‐phase fluid flow problems; however, FROST relies on MCS to compute the travel‐time distribution, which is then used to perform the transport (phase saturation) computations. To alleviate this computational bottleneck, we replace MCS with a deterministic equation for the cumulative distribution function (CDF) of travel time. The resulting CDF‐FROST approach yields the CDF of the saturation field without resorting to sampling‐based strategies. Our numerical experiments demonstrate the high accuracy of CDF‐FROST in computing the CDFs of both saturation and travel time. For the same accuracy, it is about 5 and 10 times faster than FROST and MCS, respectively.

Yang, Hyung Jun↗
DOE OSTI2023

Modeling study of divertor particle flow pattern and in–out plasma density asymmetry due to drifts with SOLPS and BOUT++

A study of the effects of drifts on the particle flow pattern and in–out divertor plasma density asymmetry for L-mode and H-mode plasmas is carried out for EAST discharges by the edge plasma transport codes SOLPS and BOUT++. The simulation of L-mode plasmas is done by SOLPS while the simulation of H-mode plasmas is done by BOUT++. The toroidal magnetic field direction for the simulated discharge is artificially reversed in the codes to study the effects of different drift directions on the divertor particle flow pattern and the in–out asymmetry of divertor plasma density. The divertor particle flows induced by diamagnetic and E × B drifts are found to have similar directions in the divertor region for the same discharge. The directions of the flows induced by drifts would be reversed with the reverse of toroidal magnetic field direction. The diamagnetic drift seems to have no effect on the in–out asymmetry of divertor plasma density due to its divergence-free nature. However, the E × B drift could result in a pronounced asymmetry of plasma density between the inner and outer divertor targets. The density in–out asymmetry caused by E × B drift is reversed with the reverse of E × B drift flow direction. Detailed analysis shows that the radial component of the E × B drift flow is the main cause of density asymmetry. The results from the simulation of H-mode plasmas with BOUT++ are similar to those of the L-mode plasmas with SOLPS except that the drift effects seem to be slightly larger in the H-mode plasmas compared to the L-mode plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Using tabulated NLTE data for Hohlraum simulations

Non-local thermodyamic equilibrium (NLTE) atomic kinetics is necessary in inertial confinement fusion Hohlraum simulations for adequately modeling high-Z walls and dopants but is computationally very expensive. We present here an approach for tabulating NLTE material data in an economical manner. Material properties for an arbitrary radiation field are provided through tabulated data for a limited set of radiation fields plus derivatives with respect to components of those radiation fields. We have implemented this method in a radiation-hydrodynamics code and compare Hohlraum simulations done with inline NLTE calculations with those done with tables constructed from the same atomic data. The results demonstrate that NLTE tables can replace inline calculations in Hohlraum simulations without a significant loss of accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Nitrogen precooling heat exchanger replacement and control system upgrade in superfluid cryoplant at CMTF

Liquid nitrogen precooling is used in most cryoplants to achieve cooldown to 80 K temperature range. In one such system at Fermilab’s CMTF superfluid cryoplant, where the helium supply directly exchanges heat with liquid nitrogen, freezing of nitrogen occurred inside the heat exchanger due to heat exchanger flow imbalance during a cryoplant trip. Trapped vapor pockets of nitrogen within the frozen heat exchanger channels were formed while warming up the heat exchanger, creating high localized pressure and subsequent damage/rupture of the heat exchanger. Replacement of the heat exchanger was done, and modifications were made in the system to rectify future occurrences. The control system was updated to bypass the heat exchanger entirely if the incoming helium stream temperature drops below 76 K. This was done by repurposing two control valves as heat exchanger bypass valves that were previously used for a redundant 80 K adsorber in the coldbox. Additional modifications were made to further prevent return of large amount of cold helium gas from cold end during abrupt cryoplant shutdown. This modification has ensured high reliability of heat exchanger with prevention of freezing of nitrogen which can damage the heat exchanger.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Characterization and structure of the human lysine-2-oxoglutarate reductase domain, a novel therapeutic target for treatment of glutaric aciduria type 1

In humans, a single enzyme 2-aminoadipic semialdehyde synthase (AASS) catalyses the initial two critical reactions in the lysine degradation pathway. This enzyme evolved to be a bifunctional enzyme with both lysine-2-oxoglutarate reductase (LOR) and saccharopine dehydrogenase domains (SDH). Moreover, AASS is a unique drug target for inborn errors of metabolism such as glutaric aciduria type 1 that arise from deficiencies downstream in the lysine degradation pathway. While work has been done to elucidate the SDH domain structurally and to develop inhibitors, neither has been done for the LOR domain. Here, we purify and characterize LOR and show that it is activated by alkylation of cysteine 414 by N-ethylmaleimide. We also provide evidence that AASS is rate-limiting upon high lysine exposure of mice. Finally, we present the crystal structure of the human LOR domain. Our combined work should enable future efforts to identify inhibitors of this novel drug target.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Permanent magnets for the return loop of the Cornell-Brookhaven energy recovery linac test accelerator

214 neodymium permanent magnets have been manufactured for the return loop of the CBETA multi-turn ERL at Cornell University. There are 5 types of quadrupole and combined-function gradient magnets using a variant of the circular Halbach design. These are made out of NdFeB material and glued into an aluminium housing with water channels for temperature stabilisation. The NdFeB wedges and magnet construction were done by outside companies, while the final “tuning” using inserts containing 64 iron rods per magnet was done at BNL over a period of about 6 months. Average relative field errors of 2.2×10 -4 were achieved on the beam region. The magnet strengths vary by type but are of order 10 T/m for quadrupole component and up to 0.3 T for the dipole. This paper reports on the field quality and timeline achieved in this production process

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Tuning the structure and superconductivity of SrNi 2⁢ P 2 by Rh substitution

The compound SrNi 2⁢ P 2 is unique among the ThCr 2 ⁢Si 2 class since it exhibits a temperature-induced transition upon cooling from an uncollapsed tetragonal (ucT) state to a one-third-collapsed orthorhombic (tcO) state where one out of every three P-rows bond across the Sr layers. This compound is also known for exhibiting bulk superconductivity below 1.4 K at ambient pressure. Here, in this paper, we report on the effects of Rh substitution in Sr⁢(Ni 1-x ⁢Rh x ) 2 ⁢P 2 on the structural and superconducting properties. We studied the variation of the nearest P-P distances as a function of the Rh fraction at room temperature, as well as its temperature dependence for selected compositions. We find that increasing the Rh fraction leads to a decrease in the transition temperature between the ucT and tcO states, until a full suppression of the tcO state for x ≥ 0.166. The superconducting transition first remains nearly insensitive to the Rh fraction, and then it increases to 2.3 K after the tcO state is fully suppressed. These results are summarized in a phase diagram, built upon the characterization by energy dispersive x-ray spectroscopy, x-ray diffraction, resistance, magnetization, and specific heat measurements done on crystalline samples with varying Rh content. The relationship between band structure, crystal structure, and superconductivity is discussed based on previously reported band structure calculations on SrRh 2 ⁢P 2 . Moreover, the effect of Rh fraction on the stress-induced structural transitions is also addressed by means of strain-stress studies done by uniaxial compression of single-crystalline micropillars of Sr⁢(Ni 1-x ⁢Rh x ) 2 ⁢P 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Nonperturbatively renormalized nucleon gluon momentum fraction in the continuum limit of N f = 2 + 1 + 1 lattice QCD

We present the nonperturbatively renormalized nucleon gluon momentum fraction using ensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ), generated by the MILC Collaboration. The calculation is done using clover fermions for the valence action with three pion masses, 220, 310, and 690 MeV, and three lattice spacings, 0.09, 0.12, and 0.15 fm. The renormalization is done using RI/MOM nonperturbative renormalization and using cluster-decomposition error reduction (CDER) to enhance the signal-to-noise ratio of the renormalization constant. We find the CDER technique is particularly important to improve the signal at the finer lattice ensembles where the lattice volume is larger. We extrapolate the gluon momentum fraction to the continuum-physical limit and obtain $\langle{x}\rangle$ g = 0.502⁢(53) stat+NPR ⁢(50) mixing in the $\overline{MS}$ scheme at 2 GeV, where first error includes the statistical error and uncertainties in nonperturbative renormalization, while the latter systematic error accounts for ignoring quark mixing. Our gluon momentum fraction is consistent with other recent lattice-QCD results at physical pion mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2022

Power System Resilience Evaluation Framework and Metric Review

Power system resilience has been an emerging hot topic in recent years to investigate the increasing threats of extreme events, such as natural disasters, severe weather, and cyberattacks. Although much research has been done to define, model, and quantify resilience from different aspects, the lack of universally accepted evaluation methods and resilience metrics makes it difficult to assess and compare resilience across different power systems, such as what is typically done in power system reliability studies. In this paper, first, we review the definitions of resilience, and we summarize two core concepts shared by most of the literature. Then, we develop a new framework to assess power system resilience from two perspectives - i.e., pre-event estimation and post-event evaluation - to capture system resilience performance in both general and specific fashions. We conduct a thorough review of existing resilience metrics and categorize them using the proposed framework, where recommendations are also proposed to capture core concepts of resilience.

power system resilience↗
DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗