Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Application Development”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SOFIA: Spectrometer Optimized for Facility Integrated Applications

Los Alamos National Laboratory researchers developed the Spectrometer Optimized for Facility Integrated Applications (SOFIA) for new capabilities in non-destructive nuclear material analysis, nuclear data measurements, and actinide science. SOFIA is the first ultra-high resolution gamma spectrometer technology based on low-temperature microcalorimeter designs. By precisely measuring the gamma rays passively emitted from radioactive and nuclear materials, SOFIA provides rapid, completely nondestructive analysis to improve the economics and timeliness of nuclear material analysis by reducing reliance on costly sampling and destructive analysis. By developing the first compact instrument based on ultrahigh resolution microcalorimeter gamma spectroscopy, the researchers intended to bring this technology into practical use in nuclear facilities and analytical laboratories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A labeled dataset for building HVAC systems operating in faulted and fault-free states

Abstract Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The emergent field of high entropy oxides: Design, prospects, challenges, and opportunities for tailoring material properties

A new class of ceramics, called entropy stabilized oxides, High Entropy Oxides (HEOs), multicomponent oxides, compositionally complex oxides, or polycation oxides, has generated considerable research interest since the first report in 2015. This multicomponent approach has created new opportunities for materials design and discovery. This Perspective will highlight some current research developments and possible applications while also providing an overview of the many successfully synthesized HEO systems to date. The polycation approach to composition development will be discussed along with a few case studies, challenges, and future possibilities afforded by this novel class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Developing a Vaccine Platform for a Balanced Mucosal Immune Response (CB11446), Project Report: Year 1

Mucosal vaccines can elicit protective immune responses at the infection site of respiratory or aerosolized pathogens. Achieving balanced mucosal and systemic responses is a key challenge in the design of mucosal vaccines, especially in terms of developing a broadly applicable vaccine delivery platform amenable to a wide range of subunit vaccine antigens. The overarching goal of the project entitled “Developing a Vaccine Platform for a Balanced Mucosal Immune Response” is to develop a pathogen-agnostic vaccine platform that elicits robust and balanced mucosal immune responses upon intranasal vaccination, in the context of a nanolipoprotein particle (NLP) platform. In year one, we investigated NLP-based vaccine formulations incorporating select immune adjuvants (Monophosphoryl Lipid A (MPLA), FSL-1, L18-muramyl dipeptide (MDP), and cholesterol-tagged ODN2006 (cCpG)) along with antigens relevant to plague (LcrV), tularemia (IglC), and Ebola virus (GP). We demonstrated the ability to prepare, characterize, and quantify these formulations. We then carried out studies in vivo using a BALB/c mouse model, comparing intranasal and intramuscular administration and systematically evaluated the resulting mucosal and systemic immune responses using ELISpot and ELISAs. All proposed tasks (Table 1) were completed within the twelve-month base period, and the established go/no go metric was successfully achieved. Numerous adjuvant:LcrV:NLP formulations were found to have statistically significant increases in IgA titers in serum and/or lung upon IN administration (compared to IM administration), while also producing robust IgG responses in serum. Some formulations also showed significant IFNγ responses using restimulated splenocytes. The data collected during the base period provide valuable insights for the future design of safe and effective mucosal subunit vaccines while also highlighting areas of research that merit further investigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Hydrogen Storage System Modeling: Public Access, Maintenance, and Enhancements

The overall vision of this project is to provide ownership and support for maintaining existing material-based hydrogen storage systems models. This includes making models accessible to the research community through a public web page and updating and enhancing storage systems models to support material developers in assessing their materials relative to DOE vehicle-level targets. Key elements for FY24: Continue to update and enhance existing models for broader application and user-friendliness. Develop tools to evaluate the performance of hydrogen storage materials developed under HyMARC activities or other fundamental hydrogen storage materials discovery research. Expand the application of current hydrogen storage models beyond light-duty vehicles to include medium-, heavy-duty, and mining vehicles and stationary application(s). Develop models for alternatives, such as LOHCs, to material-based systems (liquefied & gaseous H2) and compare for various mobile and stationary use cases.

ENERGY STORAGE,HYDROGEN↗
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Support CEED-enabled ECP applications in their preparation for Aurora/Frontier

The goal of this milestone was to help CEED-enabled ECP applications (particularly ExaSMR, MARBL, ExaAM, ExaWind and E3SM) in their preparations for the Aurora and Frontier architectures. This work included collaboration with ECP vendors and porting and optimization of CEED’s benchmarks and miniapps to early access hardware. As part of this milestone, we also made the best bake-off problems and bake-off kernel implementation from Nek, MFEM, libParanumal and the external community available in the latest libCEED release, libCEED-0.7. During the milestone period we also organized, in virtual form, the fourth CEED Annual meeting (CEED4AM) which included representatives from ECP applications, vendors and software technology projects. The specific tasks addressed in this milestone were to: (1) Work with vendors to port and run CEED benchmarks on early access systems for Aurora and Frontier; (2) Make the best BP/BK implementations from Nek, MFEM, libParanumal and external community available in libCEED; (3) Organize the next CEED Annual meeting (CEED4AM); and (4) Optimize CEED applications and miniapps for Aurora and Frontier architectures. The artifacts delivered include the next libCEED release, libCEED-0.7, and a number of developments integrated within applications to improve their GPU performance and capabilities. See the CEED website, http://ceed.exascaleproject.org and the CEED GitHub organization, http://github.com/ceed for more details.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development and Refinement of Bison Models for Higher Burnup Application

Electric Power Research Institute (EPRI) is an international research organization funded by organizations in the energy and government sectors. The company conducts research to tackle the energy challenges of its member organizations. In support of the U.S. nuclear industry, EPRI and Idaho National Laboratory were awarded a U.S. Department of Energy Technology Commercialization Fund to develop a loss of coolant accident (LOCA) analysis tool. This tool was developed to support burnup extension activities by supplementing the existing capabilities of operators of existing large light-water reactor plants. The tool couples INL developed codes Bison and RELAP5-3D for advanced simulation of LOCA behavior. The project consists of seven major tasks, one of which is the development and refinement of Bison models for higher burnup applications. This task is broken down into six targeted areas of development and refinement including: formation of high burnup structure, fuel fracture and pulverization, transient fission gas release, axial gas communication within a fuel rod, cladding ballooning and burst, and axial fuel relocation. This report summarizes the models for both the fuel and cladding in Bison for high burnup application, identifies the areas of development and refinement necessary to enhance the Bison capabilities to be used in the coupled simulation tool with RELAP5-3D. Integration and regression testing to demonstrate proper implementation of the new features in Bison is highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND:We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. METHODS:A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. FINDINGS:In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. INTERPRETATION:The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. FUNDING:Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Development of MCNP Training Modules for Safeguards Practitioners [Abstract]

The Monte-Carlo N-Particle (MCNP) software developed at LANL is the most widely used neutron transport code in the world. It is an essential tool for a variety of applications including detector development and design, nuclear fuel burnup simulation, criticality safety, and nondestructive assay system optimization. For this reason, it is indispensable within the safeguards and materials control & accountability (MC&A) communities. Multiple MCNP training courses have been created and taught over the last several decades by the MCNP development team at LANL, however there are no existing courses that cover specialized topics considered fundamental to NDA and safeguards models. To fill this gap, the MCNP team and Safeguards Science and Technology group at LANL have co-created a set of training modules customized to meet the specialized needs of the safeguards and MC&A communities. The basic modules cover concepts such as NDA system optimization, He-specific and other capture tallies, and tools for improved theoretical understanding. An advanced module was also created to cover topics including variance reduction for active interrogation simulations, use of the LANL MCNPTools post-processor, PTRAC (particle tracking) and list-mode data simulations, and fuel burnup simulations. The training modules teach to the latest and most state-of-the-art MCNP features and tools released by the development team at LANL and are intended to be taught jointly by the developers and safeguards experts. Ultimately, we hope that creation of these modules will serve to capture and convey the safeguards modeling and MCNP expertise at LANL, and that we will be able to share the modules more broadly with the MC&A and safeguards communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

BISON validation to in situ cladding burst test and high-burnup LOCA experiments

The process for developing and qualifying nuclear fuels for commercial nuclear application requires fundamental material development, characterization, and design; out-of-pile testing on unirradiated materials; integral fuel rod irradiations, testing, and postirradiation examinations; and transient analyses. The historical approach depends on the generation of large empirical datasets and series of integral fuel rod irradiations, and this approach ultimately takes ~20 years—or sometimes longer—to acquire data through extensive sequential testing. Thus, the qualification and eventual deployment of new fuel systems constitute a long process. However, recent technological advancements have provided researchers the opportunity to perform out-of-cell, in situ measurements to assess material performance for the duration of the experiment. One such example of this capability is the use of digital image coordination and thermal imaging to assess Zircaloy cladding performance under a simulated loss-of-coolant accident (LOCA) transient condition. In situ measurements generally provide high-fidelity strain, strain rates, and temperature surface maps. This is critical for the US nuclear industry, which is actively developing a technical basis to support extending the peak rod average burnup from 62 to ~75 GWd/tU and the deployment of accident-tolerant fuel. However, the US Nuclear Regulatory Commission (NRC) outlined in its research information letter several technical issues that the industry must address before extending burnup. One topic of specific interest is understanding the cladding balloon and rupture geometry during the LOCA heat-up phase. By leveraging these advanced in situ capabilities, this work used in situ data generated from a simulated LOCA to better understand high-temperature creep and its effect on Zircaloy balloon and rupture performance. Here, this work used the BISON fuel performance code to assess the high-temperature creep model predictions with in situ data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Toward Statistical Real-Time Power Fault Detection

We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.

bolted faults↗
DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Design and analysis of CXL performance models for tightly-coupled heterogeneous computing

Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X.

Cabrera, Anthony↗
DOE OSTI2022

Smart Phone Application to Compute Annual Solar Production of One Panel of a Plug-and-Play Solar Appliance (Cooperative Research and Development Final Report, CRADA Number CRD-19-00832)

The primary goal of the project is to develop a smart phone application (App) that will be available in both the Apple App Store and the Android store that displays results from the NREL's photovoltaic application, PVWatts Calculator, an application that estimates the energy production and cost of energy of grid-connected PV energy systems, and that will provide quick-response changes based on the orientation of the smart phone by the end user.

14 SOLAR ENERGY↗
DOE OSTI2022

BISON Validation to In-Situ Cladding Burst Test and High Burnup LOCA Experiments

The process to develop and qualify nuclear fuels for commercial nuclear application requires fundamental material development, characterization, and design; out-of-pile testing on unirradiated materials; integral fuel rod irradiations, testing, and post irradiation examinations (PIEs); and transient analyses. The historical approach depends on the generation of large empirical datasets and series of integral fuel rod irradiations, and this approach ultimately takes approximately 20 years—or sometimes longer—to acquire data through extensive sequential testing. Thus, the qualification and eventual deployment of new fuel systems constitute a long and drawn-out process. However, recent technological advancements have provided researchers the opportunity to perform out-of-cell, in-situ measurements to assess material performance for the duration of the experiment. One such example of this capability is the use of digital image coordination and thermal imaging to assess Zircaloy cladding performance under a simulated loss-of-coolant accident (LOCA) transient condition. In general, the in-situ measurements provide high-fidelity strain, strain rates, and temperature surface maps. This is critical for the United States nuclear industry, which is actively developing a technical basis to support extending the peak rod average burnup from 62 to ~75 GWd/tU and the deployment of accident-tolerant fuel. However, the Nuclear Regulatory Commission, through their research information letter, outlined a number of technical issues for the industry to address before extending burnup. One topic of specific interest is understanding the cladding balloon and rupture geometry during the LOCA heatup phase. Leveraging these advanced in-situ capabilities, this work intends to use insitu data generated from a simulated LOCA in the Severe Accident Test Station at Oak Ridge National Laboratory to better understand high-temperature creep and its impact on Zircaloy balloon and rupture performance. This work used the BISON fuel performance code to compare BISON’s high-temperature creep model predictions to in-situ data and identify limitations and gaps within the model. Additionally, BISON will subsequently be used to simulate relevant high-burnup LOCA tests. The results will be analyzed and compared to the available post-test data in a manner consistent with the approach outlined in the Nuclear Regulatory Commission research information letter.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A Computational Fluid Dynamic Study on Polymer Heat Exchangers

Polymer heat exchangers have been developed for the applications involving weight restrictions or chemical compatibility and fouling issues owing to the low density, anticorrosive properties, and low thermal expansion of polymers. Recently development of additive manufacturing also brings new opportunities to make polymer heat exchangers with desired design that was hard to realize before. However, due to the lower thermal conductivity polymer composite, the overall heat transfer performance is still a challenge in the polymer heat exchanger technology. In present work, a computational fluid dynamic (CFD) model has been developed to study the overall heat transfer performance of additively manufactured polymer heat exchangers. The CFD results offer an insight of fluid flow and temperature distribution in the polymer heat exchangers. This study provides a guidance not only on the polymer material selection but also on the design of polymer heat exchangers. The conclusions will be helpful to design a polymer heat exchanger whose overall heat transfer performance is comparable to a metal heat exchanger.

Zhang, Mingkan↗