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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Preliminary investigation of an uncertainty budget for uranium isotope ratio analysis using a liquid sampling—atmospheric pressure glow discharge—orbitrap mass spectrometer system

The liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source has proven to be an effective analysis tool for making uranium isotope ratio measurements when coupled to high-resolution mass spectrometers, such as the Orbitrap. While previous studies have shown the capabilities of the LS-APGD for isotope ratio determination, a systematic evaluation of the measurement uncertainty of the technique has not been conducted. To this end, the International Standards Organizations (ISO) guidelines to the expression of uncertainty in measurement (GUM analysis) have been applied to generate an uncertainty budget. For this work, a preliminary assessment derived from the GUM analysis was performed. The uncertainty in the instrument blank determination has been identified as a primary factor contributing to measurement uncertainty for the LS-APGD-Orbitrap method. These findings for the specific test case of uranium isotopic analysis will be invaluable in applications across the breadth of isotope ratio mass spectrometry performed on this unique instrumental platform.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Experimental and computational evaluation of alpha particle production from laser-driven proton–boron nuclear reaction in hole-boring scheme

The majority of studies on laser-driven proton–boron nuclear reaction is based on the measurement of α-particles with solid-state nuclear tracks detector (Cr39). However, Cr39's interpretation is difficult due to the presence of several other accelerated particles which can bias the analysis. Furthermore, in some laser irradiation geometries, cross-checking measurements are almost impossible. In this case, numerical simulations can play a very important role in supporting the experimental analysis. In our work, we exploited different laser irradiation schemes (pitcher–catcher and direct irradiation) during the same experimental campaign, and we performed numerical analysis, allowing to obtain conclusive results on laser-driven proton–boron reactions. A direct comparison of the two laser irradiation schemes, using the same laser parameters is presented.

Huault, M. (ORCID:0000000186999166)↗
DOE OSTI2021

Hard Rock Drilling Optimization Software

The main objective of the developed software is to reduce the cost per foot during drilling, in other words, optimize the drilling operational parameters in achieving optimum ROP while avoiding critical operational parameters due to either low ROP, drillstring vibration, accelerated cutter wear, or low MSE. The developed software can also be used for post-well analysis to provide insight and lessons learned for future drilling operations. Several functions are available in the software to help the user perform drilling analysis, optimization, and simulation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

MW-Class SOFC Pilot System Development

The overall objective of this project is to develop a low-cost, efficient, and reliable MWe-class Solid Oxide Fuel Cell (SOFC) power system towards commercial deployment in natural-gas fueled distributed generation applications. This report details the successful completion of all project objectives. The main goals of the project were to develop the conceptual design of a MWe-class SOFC power system, and to complete a techno-economic analysis (TEA) to determine system costs at low-volume production levels of >100MW per year. The combination of the BoP equipment cost and SOFC stack module factory cost provided the net fabrication cost of the selected 1MW SOFC system configuration. The results of the study showed that the cost target of less than $6000/kW is achievable for a FOAK 1MW SOFC system, exclusive of the first-time engineering cost. Also, the project included an exploratory analysis of the factory cost of next generation stack technology based on the Compact Solid-oxide Architecture (CSA) design at a high annual manufacturing rate of one-GW produced in a giga-factory. The BoP capital cost for one giga-watt power production was estimated using learning-curve analysis based on the historical manufacturing cost data related to similar high temperature Molten Carbonate fuel cell systems built by FuelCell Energy (FCE). The giga-factory manufactured SOFC cost estimate combined with the BoP cost at high production rates led to the estimation of entire system capital cost. Ultimately, a Cost of Electricity (COE) analysis was performed including natural gas fuel prices, installation and O&M costs, and stack replacement cost. The results of the techno-economic analysis were summarized by parametric estimation of COE for 1MW SOFC NOAK (Nth-of-a-Kind) system with natural gas prices as a variable parameter.

08 HYDROGEN↗
DOE OSTI2023

Value Proposition of Coatings or New Alloys on Hammer Wear

The goal of this Case Study was to compare the cost savings from improving the life span of parts that wear within a system to the additional material cost required to reach varying levels of improved life span. We recognize that the failure limits and system performance are representative of a single system that may or may not exist in the real world, however, our goal for this analysis was not to provide an answer for a specific system or to provide a full understanding the economics of all potential systems. The analysis was performed using relative changes from the base alloy cost and considered relative improvements in part life in order to generalize the comparison without referencing specific alloys or coatings that might be employed. Ultimately, this work provides a first check of the potential for improving wear characteristics of grinder hammers to provide a meaningful and impactful benefit to biomass preprocessing and conversion systems.

biomass conversion↗
DOE OSTI2024

Component innovations for lower cost mechanical vapor compression

Despite significant capital and operating costs, mechanical vapor compression (MVC) remains the preferred technology for challenging brine concentration applications. Here, this work seeks to assess the dependence of MVC costs on feedwater salinity and desired water recovery and to quantify the value of improved component performance or reduced component costs for reducing the levelized cost of water (LCOW) of MVC. We built a cost optimization model coupling thermophysical, heat and mass transfer, and technoeconomic models to optimize and identify low cost MVC system designs as a function of feedwater salinity and water recovery. The LCOW ranges over 3.6 to 6.1 $\$$/m 3 for seawater feed salinities of 25–150 g/kg and water recoveries of 40–80 %. We then perform sensitivity analysis on parameter inputs to isolate irreducible costs and determine high value component innovation targets. The LCOW was most sensitive to evaporator material costs and performance, including the overall heat transfer coefficient in the evaporator. Process and material innovations such as polymer-composite evaporator tubes that reduce evaporator costs by 25 % without reducing heat transfer performance by more than 10 % would result in MVC cost reductions of 8 %.

42 ENGINEERING↗
DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
DOE OSTI2023

A study of $CP$ violation in the decays $B^±$ → [$K^+ K^-π^+π^-$]$_Dh^±$ ($h$ = $K, π$) and $B^±$ → [$π^+π^-π^+π^-$]$_Dh^±$

The first study of CP violation in the decay mode $B^±$ → [$K^+ K^-π^+π^-$]$_Dh^±$, with $h$ = $K, π$, is presented, exploiting a data sample of proton–proton collisions collected by the LHCb experiment that corresponds to an integrated luminosity of 9 fb -1 . The analysis is performed in bins of phase space, which are optimised for sensitivity to local CP asymmetries. CP-violating observables that are sensitive to the angle γ of the Unitarity Triangle are determined. The analysis requires external information on charm-decay parameters, which are currently taken from an amplitude analysis of LHCb data, but can be updated in the future when direct measurements become available. Measurements are also performed of phase-space integrated observables for $B^±$ → [$K^+ K^-π^+π^-$]$_Dh^±$ and $B^±$ → [$π^+π^-π^+π^-$]$_Dh^±$ decays.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Assessment of Demographic, Genetic, and Imaging Variables Associated With Brain Resilience and Cognitive Resilience to Pathological Tau in Patients With Alzheimer Disease

Importance: Better understanding is needed of the degree to which individuals tolerate Alzheimer disease (AD)-like pathological tau with respect to brain structure (brain resilience) and cognition (cognitive resilience). Objective: To examine the demographic (age, sex, and educational level), genetic (APOE-ε4 status), and neuroimaging (white matter hyperintensities and cortical thickness) factors associated with interindividual differences in brain and cognitive resilience to tau positron emission tomography (PET) load and to changes in global cognition over time. Design, setting, an participants: In this cross-sectional, longitudinal study, tau PET was performed from June 1, 2014, to November 30, 2017, and global cognition monitored for a mean [SD] interval of 2.0 [1.8] years at 3 dementia centers in South Korea, Sweden, and the United States. The study included amyloid-β-positive participants with mild cognitive impairment or AD dementia. Data analysis was performed from October 26, 2018, to December 11, 2019. Exposures: Standard dementia screening, cognitive testing, brain magnetic resonance imaging, amyloid-β PET and cerebrospinal fluid analysis, and flortaucipir (tau) labeled with fluor-18 (18F) PET. Main outcomes and measures: Separate linear regression models were performed between whole cortex [18F]flortaucipir uptake and cortical thickness, and standardized residuals were used to obtain a measure of brain resilience. The same procedure was performed for whole cortex [18F]flortaucipir uptake vs Mini-Mental State Examination (MMSE) as a measure of cognitive resilience. Bivariate and multivariable linear regression models were conducted with age, sex, educational level, APOE-ε4 status, white matter hyperintensity volumes, and cortical thickness as independent variables and brain and cognitive resilience measures as dependent variables. Linear mixed models were performed to examine whether changes in MMSE scores over time differed as a function of a combined brain and cognitive resilience variable. Results: A total of 260 participants (145 [55.8%] female; mean [SD] age, 69.2 [9.5] years; mean [SD] MMSE score, 21.9 [5.5]) were included in the study. In multivariable models, women (standardized β = -0.15, P = .02) and young patients (standardized β = -0.20, P = .006) had greater brain resilience to pathological tau. Higher educational level (standardized β = 0.23, P < .001) and global cortical thickness (standardized β = 0.23, P < .001) were associated with greater cognitive resilience to pathological tau. Linear mixed models indicated a significant interaction of brain resilience × cognitive resilience × time on MMSE (β [SE] = -0.235 [0.111], P = .03), with steepest slopes for individuals with both low brain and cognitive resilience. Conclusions and relevance: Results of this study suggest that women and young patients with AD have relative preservation of brain structure when exposed to neocortical pathological tau. Interindividual differences in resilience to pathological tau may be important to disease progression because participants with both low brain and cognitive resilience had the most rapid cognitive decline over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Novel data analysis method for obtaining better performance from a complex 3D-printed collimator

Additively manufactured scattered beam collimators are increasingly being employed to boost the sample to cell peak signal ratio in high pressure neutron diffraction studies because of manufacturing versatility and performance improvements. We study how the measured diffraction pattern is affected by the presence of a collimator downstream of the sample, and develop a novel protocol that provides more effective background rejection. This protocol takes into account critical performance-determinants that were identified in this study, namely: (i) effectively identifying the collimator pattern on the detector; (ii) understanding the dependence of this pattern on sample and cell composition; and (iii) accurately identifying and differentiating the different regions of the pattern on the detector based on the dependency of the cell or sample and finally (iv) resolving the intensities at regions of the detector where neutrons scattered from the sample are preferentially represented, in order to boost the sample to cell peak signal ratio. Application of this novel analysis protocol is shown to increase the collimator performance over the traditional method.

3D printing↗
DOE OSTI2022

Thermal design analysis for SuperTruck II lithium-titanate battery pack

Here, this paper presents a systematic thermal management analysis for a new lithium-titanate-oxide battery pack to be installed in a SuperTruck II, Class 8 hybrid truck. The authors investigate the feasibility of mounting the battery pack inside the vehicle and air-cooling it with fans supplying conditioned air from the cabin. Moreover, the cells within each module are to be immersed in a heat-transfer fluid to improve temperature homogeneity. A multi-stage thermal analysis is performed to ensure adequate thermal regulation of the battery under the proposed design, considering installation and operational constraints. The authors perform thermal and electrical characterization tests on a single cell. Results inform the development of computational fluid dynamics models of the cells, modules, and pack. Module-level analysis shows that the cell temperatures can be maintained below their upper operational limit of 55 °C with module wall-to-air heat transfer coefficients between 10 and 20 Wm -2 K -1 . Pack-level analysis of distinct configurations determines effective airflow paths for adequate heat transfer and delivers a final battery pack design that achieves sufficient cooling and temperature uniformity. A reduced-order electrothermal model is developed to rapidly predict the transient battery performance and to develop a temperature control strategy.

25 ENERGY STORAGE↗
DOE OSTI2021

Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry

Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91–110% for the amino acids, 76–129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sequential infiltration of two-photon polymerized 3D photonic crystals for mid-IR spectroscopic applications

Photonic crystals (PhCs) are spatially organized structures with lattice parameters equivalent to the operational wavelength of light. PhCs have been subject to extensive research efforts in the last two decades and are known for controlling light propagation with applications in sensing and time-delayed communication due to the slow-light phenomenon. Despite their exceptional properties, PhCs are difficult to fabricate using planar micromachining techniques due to their periodic structures. Techniques like two-photon stereolithography have been discussed for PhC fabrication in the literature, but the inherent disadvantage of poor refractive index (RI) contrast results in limited application. In this work, we present sequential infiltration synthesis performed on two-photon stereolithographically printed 3D PhCs for infiltration with zinc oxide to increase the RI of 3D PhCs. Further, finite element analysis was performed over a range of RI contrast values to study the change in photonic bandgap (PBG) with RI contrast. The transmission spectra were recorded on 3D PhCs before and after infiltration to demonstrate the change experimentally. An increase in the PBG width and absorbance is seen postinfiltration due to enhanced RI. This work presents the first, to our knowledge, sequentially infiltrated enhanced 3D PhC fabricated with two-photon stereolithography.

3D printing↗
DOE OSTI2026

Assess The Water Resistance And Thermal Performance Of Pre-flashing Methods When Adding Continuous Insulation During Re-siding (AIRS)

Retrofitting existing buildings by adding continuous insulation during re-siding projects has become a popular method for enhancing energy efficiency, especially given the aging building stock and stricter energy codes. When properly integrated with existing window systems, continuous insulation can significantly improve thermal performance, but it also presents challenges related to water resistance and building envelope integrity. Research indicates that the interface between windows and wall assemblies is critical as improper installation or sealing can lead to water intrusion, materials deterioration, and energy loss. Moreover, studies show that fully integrating the windows with the insulation layer can reduce window heat loss by up to 40%, emphasizing the importance of optimizing window placement and sealing during retrofitting to ensure both energy efficiency and structural performance. In this study, we conducted experimental tests to evaluate the water penetration and thermal performance of two window types: an aluminum window with a 2-inch installation fin and a wood window, representing typical mid-20th century designs. Using the Heat, Air, and Humidity (HAM) chamber at Oak Ridge National Laboratory (ORNL), we assessed bulk water penetration and performed COMSOL analysis for thermal flux and examined the effectiveness of different pre-flashing methods, i.e., standard self-adhered flashing tape and high-performance flashing tape with low-expansion foam, to enhance water resistance. The results indicate that applying proper flashing techniques and moisture management strategies can mitigate these risks, improving the overall performance and durability of retrofitted buildings. Additionally, proper sealing during retrofitting is essential, as improper installation can lead to moisture issues that compromise both energy efficiency and structural integrity.

Shen, Zhenglai [ORNL]↗