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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Detecting Dwarf Galaxies Around NGC 55

Lambda-CDM predicts the existence of faint dwarf galaxies orbiting both large host galaxies, like the Milky Way, and smaller hosts like the Magellanic Clouds. However, the satellite populations around these smaller host galaxies are poorly understood from both a theoretical and observational perspective. In order to improve our understanding of the satellite populations of low-mass galaxies, the DELVE-DEEP Survey is performing a search for satellites around the nearby, low-mass galaxy, NGC 55. As part of this search, we aim to characterize our detection sensitivity by injecting artificial dwarf galaxies into our data set and attempting to recover them. In this poster, I present our methods of injection and recovery, as well as our preliminary results showing our measured detection rates for artificial dwarf galaxies of varying size and luminosity. Additionally, we explore the blending of resolved stellar fluxes that results as a consequence of source recovery and how this affects the recovered magnitudes of injected dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog

Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of 146 million injections spanning the entire 5000 deg 2 DES footprint, generated using the Balrog SSI pipeline. Through this SSI sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Lastly, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis, such as estimates of the redshift distribution and lens magnification. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Guide to Nondestructive Evaluation Techniques

Nondestructive evaluation (NDE), alternately known as nondestructive examination (NDE), (commonly used within the nuclear industry), nondestructive testing (NDT) (commonly used in the aerospace and petrochemical industry), and nondestructive inspection (NDI) (commonly used within the military), are as the name implies test and assessment technologies designed to detect and characterize components without damaging or compromising the component’s ability to perform its design function. The most common application is detection of flaws caused either by manufacturing anomalies, service or environmental stresses, or natural material aging. More generally, applications may include estimation of mechanical and material properties, stress/strain, and dynamic response behavior. Post-manufacturing inspections frequently enjoy unfettered access to the component or part that may allow more thorough examinations, while in-service examinations after components have been in use may impose severe access limitations for in-situ testing. If flaws are detected, they must then be evaluated relative to their size, location, growth rate, growth mechanism, and likelihood that they are or will become a liability to the component’s function or performance. NDE is comprised of a large family of specific test disciplines including visual inspection, dimensional metrology, ultrasound, radiography, penetrant test, magnetic particle test, leak test, eddy current tests, potential drop tests, flash and vibro thermography, shearography, acoustic emissions, and many other methods. This section provides discussion on general NDE science and considerations for specific technique selection.

nondestructive, examination, inspection, NDE, NDT↗
DOE OSTI2024

Recycled graphite enabled superior performance for lithium ion batteries

Recycling graphite attracts growing attention since cumulative amount of spent Li-ion batteries and the shortage of graphite supply chain. Although various recycling methods have been reported, the recycled graphite cannot reach the strict commercial standards of purity, scalability, efficiency, and capacity, preventing it from battery manufacturing. Herein, the important roles of defects and functional groups on the graphite surface are deeply studied, and a closed-loop graphite recycling process with the surface recovery and modification for the graphite from the end-of-life batteries is demonstrated. The recovered graphite delivers a purity of over 99.9 % and an average initial coulombic efficiency of 91.5 %. Compared with commercial graphite in industrial standard battery testing parameters, full cells with recovered graphite possess enhanced rate reversibility, doubled cycle life, over 10 % higher capacity along with half anode material cost. In conclusion, these impressive results not only underscore the transformative potential of surface reconstruction and modification in graphite recycling, but also present economic feasibility and sustainable pathway for significantly improving battery performance and addressing global resource challenges via integration with the hydrometallurgical recycling process.

25 ENERGY STORAGE↗
DOE OSTI2018

LADR: low-cost application-level detector for reducing silent output corruptions

Applications running on future high performance computing (HPC) systems are more likely to experience transient faults due to technology scaling trends with respect to higher circuit density, smaller transistor size and near-threshold voltage (NTV) operations. A transient fault could corrupt application state without warning, possibly leading to incorrect application output. Such errors are called silent data corruptions (SDCs). In this paper, we present LADR, a low-cost application-level SDC detector for scientific applications. LADR protects scientific applications from SDCs by watching for data anomalies in their state variables (those of scientific interest). It employs compile-time data-flow analysis to minimize the number of monitored variables, thereby reducing runtime and memory overheads while maintaining a high level of fault coverage with low false positive rates. We evaluated LADR with 4 scientific workloads and results show that LADR achieved < 80% fault coverage with only ∼ 3% runtime overheads and ∼ 1% memory overheads. As compared to prior state-of-the-art anomaly-based detection methods, SDC achieved comparable or improved fault coverage, but reduced runtime overheads by 21% ∼ 75%, and memory overheads by 35% ∼ 55% for the evaluated workloads. We believe that such an approach with low memory and runtime overheads coupled with attractive detection precision makes LADR a viable approach for assuring the correct output from large-scale high performance simulations.

Chen, Chao↗
DOE OSTI2024

Computational study on the impact of gasoline-ethanol blending on autoignition and soot/NO x emissions under low-load gasoline compression ignition conditions

Here, in the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NO x ), and soot emissions under low-load conditions. In order to represent the test gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF)+ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed. A three-dimensional (3D) engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism (including NO x sub-mechanism), adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture the in-cylinder combustion phenomena and soot/NO x emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for a broad range of start-of-injection (SOI) timings (−21, −27, −36, and −45 crank angle degrees (°CA) after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NO x and soot emissions. For relatively later injection timings (−21 and −27 °CA aTDC), E30 yields higher amount of soot than E10; while the trend reverses for early injection cases (−36 and −45 °CA aTDC ). On the other hand, E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NO x emissions from various fuels follow a trend of E10>E30>E100. On the other hand, NO x emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NO x decreases when SOI is advanced beyond −36 °CA aTDC. Detailed analysis of the numerical results is performed to investigate the soot/NO x emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

Computational fluid dynamics↗
DOE OSTI2020

Feedback on Forty-year Long Clean-up Operations of a Contaminated Soil for Environmental Purpose - 20026

One of the CEA facilities in France is the place of miscellaneous mapping and clean-up operations since the 1980's. The final purpose of the conducted work deals with the environmental remediation of the building. In France, the absence of a regulatory framework for the management of sites polluted by radioactive substances, especially as the lack of release thresholds, has led to develop different approaches of soil radiological characterization. The geostatistical approach is part of them. Preliminary investigations are an unavoidable step prior to clearance and remediation. First, historical analysis enables to define site perimeters to investigate as well as to precisely locate expected contaminated parts. For radioactive pollutants, the media (water, soil, air) and transfer routes participate in the definition of the investigation perimeter and thus also need to be identified through a geological study. At the same time, functional analysis leads to choose the best measurement means which could provide useful information when combined with visual inspection. An equipment well adapted to easy-to-measure radionuclides as gamma emitters, such as gamma probe or on-site gamma spectrometry, is usually selected as a non-destructive assay. Coupled with spatial positions, processed on-site radiological data can be mapped. Data processing consists in mapping and kriging before a geostatistical analysis in order to identify the zones of interest to be targeted. Selected zones are not only linked to high level count rates but also to low level ones so that a pollution-free reference is known in the area. This data processing also enables to categorize waste types according to their origin and contamination levels. An expected volume of different waste then follows as well as the waste characterization equipments. In-depth investigations, e.g. core drillings and samplings, naturally ensue to aim at remediation optimization through the assessment of environmental impact. After clean-up operations, non-destructive assays as well as destructive samplings enable to check the effective clearance. Following this methodology in the present remediation work, results of the first non-destructive assay campaign highlighted a long-lived transuranic radionuclides contamination in the concrete flagstone. Successive non-destructive assays were then performed in the defined zones of interest. Count rates measured with a surface probe were first mapped. The best localization of destructive assays, i.e. core drillings at a 1 meter depth, came from this surface mapping followed by a geostatistical analysis. The concrete flagstone was totally removed. Then, the depth of soil to remove under the flagstone was optimized from the results of radiological activities measured in core drillings samples. A new surface mapping was then drawn after this partial soil excavation. The sand and demolition rubble samples, homogeneously constituted from the flagstone fragments, were analyzed by gamma spectrometry. Meanwhile, waste drums and other containers were also measured by gamma spectrometry and passive neutron measurement devices. All these results have brought miscellaneous pieces of information. In this context, major issues appear in terms of physical constraints such as the premises tininess as well as of soil sampling techniques and in terms of measurement performances assessment and the representativeness of homogeneous samples. The choice of radiological soil and waste characterization devices is described in the light of enhanced performances by keeping in mind radiation protection requirements and a willing to always optimize waste categories (very low level waste) and volume. The improvement of the characterization methods appears through this forty-year long work while sharing the feedback of encountered difficulties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
DOE OSTI2018

Colloidal Cobalt Phosphide Nanocrystals as Trifunctional Electrocatalysts for Overall Water Splitting Powered by a Zinc–Air Battery

Highly efficient and stable electrocatalysts, particularly those that are capable of multifunctionality in the same electrolyte, are in high demand for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR). In this paper, highly monodisperse CoP and Co 2 P nanocrystals (NCs) are synthesized using a robust solution-phase method. The highly exposed (211) crystal plane and abundant surface phosphide atoms make the CoP NCs efficient catalysts toward ORR and HER, while metal-rich Co 2 P NCs show higher OER performance owing to easier formation of plentiful Co 2 P@COOH heterojunctions. Density functional theory calculation results indicate that the desorption of OH* from cobalt sites is the rate-limiting step for both CoP and Co 2 P in ORR and that the high content of phosphide can lower the reaction barrier. A water electrolyzer constructed with a CoP NC cathode and a Co 2 P NC anode can achieve a current density of 10 mA cm -2 at 1.56 V, comparable even to the noble metal-based Pt/C and RuO 2 /C pair. Finally and furthermore, the CoP NCs are employed as an air cathode in a primary zinc–air battery, exhibiting a high power density of 62 mW cm -2 and good stability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗
DOE OSTI2019

A Novel Technique for the Treatment of Type 2 Endoleak After Endovascular Aortic Repair: Sac Embolization with Balloon Occlusion of the Aorta (SEBOA)

Purpose: We presented a new method of sac embolization using n-butyl-cyanoacrylate (NBCA) with balloon occlusion of the aorta (SEBOA) that can facilitate decreasing flow rate of the involved branches with the goal of type 2 endoleak resolution after endovascular aortic repair (EVAR). Technique: This technique is demonstrated in six patients who required type 2 endoleak treatment including previous technical failure. A transarterial approach was performed in four patients and transabdominal direct puncture in two. Technical success was defined as complete embolization of both involved branches and sac on postoperative CT. Sacography under balloon occlusion of the aorta demonstrated decreased flow rate of the all involved branches in all patients. SEBOA was performed using 25 or 33% of NBCA diluted with lipiodol. Technical success was obtained in 3 of 6 patients, and one major complication was observed with adhesion of NBCA to the microcatheter resulting in foreign body retention. Conclusion: SEBOA may help solve the difficulty of type 2 endoleak treatment after EVAR as decreased flow rate of the involved branches under balloon occlusion of the aorta was achieved in all patients. However, protocols regarding concentration of NBCA or using other embolic materials are needed to improve the success rate.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A quasi-static Monte Carlo algorithm for the simulation of sub-prompt critical transients

A pseudo-time-dependent Monte Carlo algorithm suitable for simulating thermal reactor transients with timescales on the order of seconds is presented. An initial experimental implementation has been written into the Monte Carlo Application ToolKit (MCATK) developed at Los Alamos National Laboratory. The algorithm is essentially a combination of the fully time-dependent Monte Carlo algorithm, including the explicit simulation of delayed neutron precursors, and a quasi-static algorithm that does not use the point-kinetics equations. The fully time-dependent calculation is performed for some number of inactive and active micro time steps. The dynamic reactor time constant and delayed neutron precursor production rates are calculated directly from the active cycles, rather than from point-kinetics parameters. These quantities are then used to advance the neutron population and delayed neutron precursors forward in time across a macro time step. As a result, the method is applied to the C5G7-TD benchmark operating in steady state and in transient mode.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Sentinel Lymph Node Dissection to Select Clinically Node-negative Prostate Cancer Patients for Pelvic Radiation Therapy: Effect on Biochemical Recurrence and Systemic Progression

Purpose: To assess the efficacy of robotic-assisted laparoscopic sentinel lymph node (SLN) dissection (SLND) to select those patients with prostate cancer (PCa) who would benefit from additional pelvic external beam radiation therapy and long-term androgen deprivation therapy (ADT). Methods and Materials: Radioisotope-guided SLND was performed in 224 clinically node-negative patients scheduled to undergo external beam radiation therapy. Patients with histologically positive SLNs (pN1) were also offered radiation therapy to the pelvic lymph nodes, combined with 3 years of ADT. Biochemical recurrence (BCR), overall survival, and metastasis-free (including pelvic and nonregional lymph nodes) survival (MFS) rates were retrospectively calculated. The Briganti and Kattan nomogram predictions were compared with the observed pN status and BCR. Results: The median prostate-specific antigen (PSA) value was 15.4 ng/mL (interquartile range [IQR] 8-29). A total number of 834 SLNs (median 3 per patient; IQR 2-5) were removed. Nodal metastases were diagnosed in 42% of the patients, with 150 SLNs affected (median 1; IQR 1-2). The 5-year BCR-free and MFS rates for pN0 patients were 67.9% and 87.8%, respectively. The corresponding values for pN1 patients were 43% and 66.6%. The PSA level and number of removed SLNs were independent predictors of BCR and MFS, and pN status was an additional independent predictor of BCR. The 5-year overall survival rate was 97.6% and correlated only with pN status. The predictive accuracy of the Briganti nomogram was 0.665. Patients in the higher quartiles of Kattan nomogram prediction of BCR had better than expected outcomes. The complication rate from SLND was 8.9%. Conclusions: For radioisotope-guided SLND, the high staging accuracy is accompanied by low morbidity. The better than expected outcomes observed in the lower quartiles of BCR prediction suggest a role for SLN biopsy as a potential selection tool for the addition of pelvic radiation therapy and ADT intensification in pN1 patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Hierarchical NiCo{sub 2}O{sub 4} nanowalls composed of ultrathin nanosheets as electrode materials for supercapacitor and Li ion battery applications

Highlights: • NiCo{sub 2}O{sub 4} nanowalls composed of ultrathin nanosheets are synthesized by solvothermal reaction. • The prepared NiCo{sub 2}O{sub 4} structures possess excellent electrochemical properies. • The hierarchica electrode materials might potential applications for energy storage devices. - Abstract: In this work, NiCo{sub 2}O{sub 4} nanowalls composed of ultrathin nanosheets are fabricated successfully on Ni foam by a facile one-pot solvothermal method followed by thermolysis of the precursors. The as-prepared NiCo{sub 2}O{sub 4} products are evaluated as electrode materials for supercapacitors and lithium ion batteries. The results show high discharge capacitance of 925 mF/cm{sup 2} at a current density of 5 mA/cm{sup 2}, with remarkable cycling stability (about 24.2% loss after 6000 cycles) and excellent rate performance (71.5% capacitance retention with current density range from 1 mA/cm{sup 2} to 20 mA/cm{sup 2}). In addition, Ni foam supported NiCo{sub 2}O{sub 4} nanowalls possess excellent discharge capacity of 1100 mAh/g as lithium ion battery anode at a current density of 100 mA/g. The excellent electrochemical performance of NiCo{sub 2}O{sub 4} nanowalls could be ascribed to the unique spatial structure composed of interconnected ultrathin nanosheets, which facilitates ion penetration and electron transportation, demonstrating their potential applications as electrode materials for high performance supercapacitors and lithium ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Investigation of partitionless growth of ε -Al 60 Sm 11 phase in Al-10 at% Sm liquid

Recent experiments on devitrification of Al 90 Sm 10 amorphous alloys revealed an unusual polymorphic transformation to a complex cubic crystal structure called the ε-Al 60 Sm 11 phase. Molecular dynamics simulations of the growth of the stoichiometric ε-phase seed in contact with an undercooled Al-10 at% Sm liquid are performed to elucidate the microscopic process of transformation. The as-grown crystal and undercooled liquid possess similar local order around Al atoms whereas a rigid network defined by the Sm sub-lattice develops during the growth. Using a template-cluster alignment method, we define an order parameter to characterize the structural evolution in the system. Estimates of the attachment rate is ${R}_{{\rm{a}}}=8.70\times {10}^{-4}$ Å-2 ns-1 and detachment rate is ${R}_{{\rm{d}}}=3.83\times {10}^{-4}$ Å-2 ns-1 at the interface between ε-Al 60 Sm 11 and Al-10 at% Sm liquid at 800 K.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Proposal for superconducting quantum networks using multioctave transduction to lower frequencies

We propose networking superconducting quantum circuits by transducing their excitations (typically 4–8 GHz) to 200–500 MHz photons for transmission via superconducting cryogenic coaxial cables. This frequency down-conversion reduces transmission losses and, counterintuitively, reduces noise. We introduce a multioctave asymmetrically threaded superconducting quantum interference device circuit capable of the required efficient, high-rate transduction. For a 100-meter cable with 𝑄 𝑖 = 10 5 at 10 mK, we theoretically show that our approach achieves single-photon fidelities of 0.962 at 200 MHz versus 0.772 at 8 GHz, and triples the lower bound on quantum channel capacity. In conclusion, this method enables highly efficient networking of superconducting qubits while maintaining high fidelities, combining improved performance with the practical advantages of flexible, compact coaxial cables.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Sci-Thur AM: YIS – 08: Automated Imaging Quality Assurance for Image-Guided Small Animal Irradiators

Purpose: To develop quality assurance (QA) standards and tolerance levels for image quality of small animal irradiators. Methods: A fully automated in-house QA software for image analysis of a commercial microCT phantom was created. Quantitative analyses of CT linearity, signal-to-noise ratio (SNR), uniformity and noise, geometric accuracy, modulation transfer function (MTF), and CT number evaluation was performed. Phantom microCT scans from seven institutions acquired with varying parameters (kVp, mA, time, voxel size, and frame rate) and five irradiator units (Xstrahl SARRP, PXI X-RAD 225Cx, PXI X-RAD SmART, GE explore CT/RT 140, and GE Explore CT 120) were analyzed. Multi-institutional data sets were compared using our in-house software to establish pass/fail criteria for each QA test. Results: CT linearity (R2>0.996) was excellent at all but Institution 2. Acceptable SNR (>35) and noise levels (<55HU) were obtained at four of the seven institutions, where failing scans were acquired with less than 120mAs. Acceptable MTF (>1.5 lp/mm for MTF=0.2) was obtained at all but Institution 6 due to the largest scan voxel size (0.35mm). The geometric accuracy passed (<1.5%) at five of the seven institutions. Conclusion: Our QA software can be used to rapidly perform quantitative imaging QA for small animal irradiators, accumulate results over time, and display possible changes in imaging functionality from its original performance and/or from the recommended tolerance levels. This tool will aid researchers in maintaining high image quality, enabling precise conformal dose delivery to small animals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

36 MATERIALS SCIENCE↗