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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 OSTI2016

Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows, In: ISAV '16 Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization

The simulation of multiscale physics is an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. To address this, we present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. All analyses are performed in parallel using distributed memory and use the same domain decomposition as the simulation. A task management framework is constructed to leverage available parallelism within a node for analysis. The capabilities of the framework are to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and minimize the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid-phase flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent analysis over fluid sub-regions determined from connected components analysis. Simulations performed on Oak Ridge National Lab's Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis approach for typical production simulation of two-fluid-phase flow.

Computer Science↗
DOE OSTI2014

FTP 100: An Ultra-High Speed Data Transfer Service Over Next Generation 100 Gigabit Per Second Network

Data-intensive applications, including high energy and nuclear physics, astrophysics, climate modeling, nano-scale materials science, genomics, and financing, are expected to generate exabytes of data over the coming years, which must be transferred, visualized, and analyzed by geographically distributed teams of users. High-performance network capabilities must be available to these users at the application level in a transparent, virtualized manner. Moreover, the application users must have the capability to move large datasets from local and remote locations across network environments to their home institutions. To solve these challenges, the main goal of our project is to design and evaluate high-performance data transfer software to support various data-intensive applications. First, we have designed a middleware software that provides access to Remote Direct Memory Access (RDMA) functionalities. This middleware integrates network access, memory management and multitasking in its core design. We address a number of issues related to its efficient implementation, for instance, explicit buffer management and memory registration, and parallelization of RDMA operations, which are vital to delivering the benefit of RDMA to the applications. Built on top of this middleware, an implementation and experimental evaluation of the RDMA-based FTP software, RFTP, is described and evaluated. This application has been implemented by our team to exploit the full capabilities of advanced RDMA mechanisms for ultra-high speed bulk data transfer applications on Energy Sciences Network (ESnet). Second, we designed our data transfer software to optimize TCP/IP based data transfer performance such that RFTP can be fully compatible with today’s Internet. Our kernel optimization techniques with Linux system calls sendfile and splice, can reduce data copy cost. In this report, we summarize the technical challenges of our project, the primary software design methods, the major project milestones achieved, as well as the testbed evaluation work and demonstrations during our project life time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Case Study for the ARRA-funded Ground Source Heat Pump (GSHP) Demonstration at Wilders Grove Solid Waste Service Center in Raleigh, NC

High initial costs and lack of public awareness of ground-source heat pump (GSHP) technology are the two major barriers preventing rapid deployment of this energy-saving technology in the United States. Under the American Recovery and Reinvestment Act (ARRA), 26 GSHP projects have been competitively selected and carried out to demonstrate the benefits of GSHP systems and innovative technologies for cost reduction and/or performance improvement. This paper highlights the findings of a case study of one of the ARRA-funded GSHP demonstration projects, a distributed GSHP system for providing all the space conditioning, outdoor air ventilation, and 100% domestic hot water to the Wilders Grove Solid Waste Service Center of City of Raleigh, North Carolina. This case study is based on the analysis of measured performance data, construction costs, and simulations of the energy consumption of conventional central heating, ventilation, and air-conditioning (HVAC) systems providing the same level of space conditioning and outdoor air ventilation as the demonstrated GSHP system. The evaluated performance metrics include the energy efficiency of the heat pump equipment and the overall GSHP system, pumping performance, energy savings, carbon emission reductions, and cost-effectiveness of the GSHP system compared with conventional HVAC systems. This case study also identified opportunities for reducing uncertainties in the performance evaluation and improving the operational efficiency of the demonstrated GSHP system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Discovering the Whole by the Coarse: A topological paradigm for data analysis

The increasing interest in big data applications is ushering in a large effort in seeking new, efficient, and adapted data models to reduce complexity, while preserving maximal intrinsic information. Graph-based models have recently been getting a lot of attention on account of their intuitive and direct connection to the data. The cost of these models, however, is to some extent giving up geometric insight as well as algebraic flexibility. Topology, as an intermediate analysis medium, focuses on coarse structures of an object/signal in general. It affords a formalism of transitioning from a local to a global description of an object, while providing significant information, which respects the local structure of measurements. It may also support a global visualization (e.g., data variation trends) and enhances the understanding of the underlying phenomenon. Throughout this article, measurements may be considered proximal in various ways, depending on the specific application, and the selected proximity metrics are subsequently used in the construction of a graph structure, thus highlighting various groups of neighboring data. As described later, these groups, often based on proximity criteria, are represented by n-simplices, where n + 1 corresponds to the number of points in an associated group. Collectively, they form what is (and will be throughout) referred to as a simplicial complex, a special representation of a topological space. We find the key advantage of computational topology is its inherently algebraic structure on the basic elements of the resulting graph-like structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Using Map Service API for Driving Cycle Detection for Wearable GPS Data: Preprint

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method's ability to detect vehicle mode accurately.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
NASA NTRS1993

High-speed data duplication/data distribution: An adjunct to the mass storage equation

The term 'mass storage' invokes the image of large on-site disk and tape farms which contain huge quantities of low- to medium-access data. Although the cost of such bulk storage is recognized, the cost of the bulk distribution of this data rarely is given much attention. Mass data distribution becomes an even more acute problem if the bulk data is part of a national or international system. If the bulk data distribution is to travel from one large data center to another large data center then fiber-optic cables or the use of satellite channels is feasible. However, if the distribution must be disseminated from a central site to a number of much smaller, and, perhaps varying sites, then cost prohibits the use of fiber-optic cable or satellite communication. Given these cost constraints much of the bulk distribution of data will continue to be disseminated via inexpensive magnetic tape using the various next day postal service options. For non-transmitted bulk data, our working hypotheses are that the desired duplication efficiency of the total bulk data should be established before selecting any particular data duplication system; and, that the data duplication algorithm should be determined before any bulk data duplication method is selected.

Howard, Kevin↗
DOE OSTI2016

Capturing the Impact of Storage and Other Flexible Technologies on Electric System Planning

Power systems of the future are likely to require additional flexibility. This has been well studied from an operational perspective, but has been more difficult to incorporate into capacity expansion models (CEMs) that study investment decisions on the decadal scale. There are two primary reasons for this. First, the necessary input data, including cost and resource projections, for flexibility options like demand response and storage are significantly uncertain. Second, it is computationally difficult to represent both investment and operational decisions in detail, the latter being necessary to properly value system flexibility, in CEMs for realistically sized systems. In this work, we extend a particular CEM, NREL's Resource Planning Model (RPM), to address the latter issue by better representing variable generation impacts on operations, and then adding two flexible technologies to RPM's suite of investment decisions: interruptible load and utility-scale storage. This work does not develop full suites of input data for these technologies, but is rather methodological and exploratory in nature. We thus exercise these new investment decisions in the context of exploring price points and value streams needed for significant deployment in the Western Interconnection by 2030. Our study of interruptible load finds significant variation by location, year, and overall system conditions. Some locations find no system need for interruptible load even with low costs, while others build the most expensive resources offered. System needs can include planning reserve capacity needs to ensure resource adequacy, but there are also particular cases in which spinning reserve requirements drive deployment. Utility-scale storage is found to require deep cost reductions to achieve wide deployment and is found to be more valuable in some locations with greater renewable deployment. Differences between more solar- and wind-reliant regions are also found: Storage technologies with lower energy capacities are deployed to support solar deployment, and higher energy capacity technologies support wind. Finally, we identify potential future research and areas of improvement to build on this initial analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
NASA NTRSDate not supplied

BioSentinel: Leading the Way for Deep Space CubeSat Missions

Flagship science missions are not alone in Deep Space thanks to BioSentinel, a 6U spacecraft launched on Artemis-1. BioSentinel is one of the longest operating CubeSats beyond cislunar space. The subsystems and COTS components of the BioSentinel bus are a template for future deep space missions, and the lessons learned from over a year of operations will enable improved performance for the next missions. BioSentinel achieved its unprecedented performance for an SLS secondary payload due to preparation, planning, and a robust design. Pre-launch antenna and interface testing with both DSN and ESA confirmed command and data pathways and allowed for operational flexibility in the critical early hours post-deployment. Mission Operations simulations prior to launch identified potential risks and primed operators to respond in flight, preparing the team to react quickly to successfully detumble the spacecraft and enter a power-positive state. The spacecraft would not have survived without the inclusion of the trailblazing 3D-printed composite cold gas propulsion system. The non-standard tank geometry enabled efficient use of the limited space available in the CubeSat, as well as the capability to detumble the spacecraft and manage momentum, while providing sufficient margin to execute potential delta-V maneuvers. The Iris radio has operated for over 18 months with no significant issues. Initial Iris performance estimates have been accurate throughout the mission. BioSentinel continues to collect data on thermal conditions and to validate our performance models with real-world knowledge. We have received exemplary support from our DSN partners. Following the conclusion of the primary science mission, the Linear Energy Transfer (LET) Spectrometer continued to collect solar and galactic radiation data from its location in heliocentric orbit. The free space dataset offered by the BioSentinel LET is a valuable source of data for model validation and future mission planning. As the spacecraft travels farther from Earth it is poised to provide longitudinally distributed measurements of solar particle events during solar maximum. The lessons learned from BioSentinel suggest key areas to enhance performance. The ability to upload modified flight software can increase the stability of memory management. Additional heaters in the propulsion system design have already proven successful on the Starling mission. Streamlining mission operations can reduce costs, increase data return, and better utilize DSN time. Enhancements such as these will facilitate reliable, long-duration deep space exploration using the proven BioSentinel 6U CubeSat bus.

BioSentinel↗
NASA NTRS1999

Overview of the Landsat-7 Mission

Landsat-7 is scheduled for launch on April 15 from the Western Test Range at Vandenberg Air Force Base, Calif., on a Delta-H expendable launch vehicle. The Landsat 7 satellite consists of a spacecraft bus being provided by Lockheed Martin Missiles and Space (Valley Forge, Pa.) and the Enhanced Thematic Mapper Plus instrument built by Raytheon (formerly Hughes) Santa Barbara Remote Sensing (Santa Barbara, Calif.). The instrument on board Landsat 7 is the Enhanced Thematic Mapper Plus (ETM+). ETM+ improves upon the previous Thematic Mapper (TM) instruments on Landsat's 4 and 5 (Fig. la and lb). It includes the previous 7 spectral bands measuring reflected solar radiation and emitted thermal emissions but, in addition, includes a new 15 in panchromatic (visible-near infrared) band. The spatial resolution of the thermal infrared band has also been improved to 60 m. Both the radiometric precision and accuracy of the sensor are also improved from the previous TM sensors. After being launched into a sun-synchronous polar orbit, the satellite will use on-board propulsion to adjust its orbit to a circular altitude of 438 miles (705 kilometers) crossing the equator at approximately 10 a.m. on its southward track. This orbit will place Landsat 7 along the same ground track as previous Landsat satellites. The orbit will be maintained with periodic adjustments for the life of the mission. A three-axis attitude control subsystem will stabilize the satellite and keep the instrument pointed toward the Earth to within 0.05 degrees. Later this year, plans call for the NASA Earth Observation System (EOS) Terra (AM-1) observatory and the experimental EO-1 mission to closely follow Landsat-7's orbit to support synergistic research and applications from this new suite of terrestrial sensor systems. Landsat is the United States' oldest land-surface observation satellite system, with satellites continuously operating since 1972. Although the program has scored numerous successes in scientific and resource-management applications, Landsat has had a tumultuous history of management and funding changes over its nearly 27-year history. Landsat-7 marks a new direction in the program to reduce the cost of data and increase systematic global coverage for use in global change research as well as commercial and regional applications. With the passage of the Land Remote Sensing Policy Act in 1992, oversight of the Landsat program began to shift from the commercial sector to the federal government. NASA integrated Landsat-7 into its EOS science program in 1994. Landsat-7 is managed and operated jointly by NASA and U.S. Geological Survey (USGS). As a result, the costs of acquiring observations from

Williams, Darrel↗
DOE OSTI2012

Hot wire needle probe for in-reactor thermal conductivity measurement

Thermal conductivity is a key property that must be known for proper design, test, and application of new fuels and structural materials in nuclear reactors. Thermal conductivity is highly dependent on the physical structure, chemical composition, and the state of the material. Typically, thermal conductivity changes that occur during irradiation are measured out-of-pile by Post Irradiated Examination (PIE) using a “cook and look” approach in hot-cells. Repeatedly removing samples from a test reactor to make out-of-pile measurements is expensive, has the potential to disturb phenomena of interest, and only provides understanding of the sample's end state at the time each measurement is made. There are also limited thermophysical property data for advanced fuels. Such data are needed for simulation design codes, the development of next generation reactors, and advanced fuels for existing nuclear plants. Being able to quickly characterize fuel thermal conductivity during irradiation can improve the fidelity of data, reduce costs of post-irradiation examinations, increase understanding of how fuels behave under irradiation, and confirm or improve existing thermal conductivity measurement techniques. This paper discusses recent efforts to develop and evaluate an in-pile thermal conductivity sensor based on a hot wire needle probe. Testing has been performed on samples with thermal conductivities ranging from 0.2 W/m-K to 22 W-m-K in temperatures ranging from 20 °C to 600 °C. Thermal conductivity values measured using the needle probe match data found in the literature to within 5% for samples tested at room temperature, 5.67% for low thermal conductivity samples tested at high temperatures, and 10% for high thermal conductivity samples tested at high temperatures. Experimental results also show that this sensor is capable of operating in various test conditions and of surviving long duration irradiations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Assessment of the polygeneration approach in wastewater treatment plants for enhanced energy efficiency and green hydrogen/ammonia production

Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. Here, this study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H 2 ) and ammonia (NH 3 ) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH 3 production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH 3 production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH 3 production. Scenario 3, in particular, achieves competitive H 2 and NH 3 production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO 2eq , with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

42 ENGINEERING↗
NASA NTRS1980

Cogeneration Technology Alternatives Study (CTAS). Volume 3: Energy conversion system characteristics

Six current and thirty-six advanced energy conversion systems were defined and combined with appropriate balance of plant equipment. Twenty-six industrial processes were selected from among the high energy consuming industries to serve as a frame work for the study. Each conversion system was analyzed as a cogenerator with each industrial plant. Fuel consumption, costs, and environmental intrusion were evaluated and compared to corresponding traditional values. The advanced energy conversion technologies indicated reduced fuel consumption, costs, and emissions. Fuel energy savings of 10 to 25 percent were predicted compared to traditional on site furnaces and utility electricity. With the variety of industrial requirements, each advanced technology had attractive applications. Fuel cells indicated the greatest fuel energy savings and emission reductions. Gas turbines and combined cycles indicated high overall annual savings. Steam turbines and gas turbines produced high estimated returns. In some applications, diesels were most efficient. The advanced technologies used coal derived fuels, or coal with advanced fluid bed combustion or on site gasifications. Data and information for both current and advanced energy conversion technology are presented. Schematic and physical descriptions, performance data, equipment cost estimates, and predicted emissions are included. Technical developments which are needed to achieve commercialization in the 1985-2000 period are identified.

Source record↗
DOE OSTI2014

Genetic variation in the CYP1A1 gene is related to circulating PCB118 levels in a population-based sample

Several of the polychlorinated biphenyls (PCBs), i.e. the dioxin-like PCBs, are known to induce the P450 enzymes CYP1A1, CYP1A2 and CYP1B1 by activating the aryl hydrocarbon receptor (Ah)-receptor. We evaluated if circulating levels of PCBs in a population sample were related to genetic variation in the genes encoding these CYPs. In the population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (1016 subjects all aged 70), 21 SNPs in the CYP1A1, CYP1A2 and CYP1B1 genes were genotyped. Sixteen PCB congeners were analysed by high-resolution chromatography coupled to high-resolution mass spectrometry (HRGC/ HRMS). Of the investigated relationships between SNPs in the CYP1A1, CYP1A2 and CYP1B1 and six PCBs (congeners 118, 126, 156, 169, 170 and 206) that captures >80% of the variation of all PCBs measured, only the relationship between CYP1A1 rs2470893 was significantly related to PCB118 levels following strict adjustment for multiple testing (p=0.00011). However, there were several additional SNPs in the CYP1A2 and CYP1B1 that showed nominally significant associations with PCB118 levels (p-values in the 0.003–0.05 range). Further, several SNPs in the CYP1B1 gene were related to both PCB156 and PCB206 with p-values in the 0.005–0.05 range. Very few associations with p<0.05 were seen for PCB126, PCB169 or PCB170. Genetic variation in the CYP1A1 was related to circulating PCB118 levels in the general elderly population. Genetic variation in CYP1A2 and CYP1B1 might also be associated with other PCBs. - Highlights: • We studied the relationship between PCBs and the genetic variation in the CYP genes. • Cross sectional data from a cohort of elderly were analysed. • The PCB levels were evaluated versus 21 SNPs in three CYP genes. • PCB 118 was related to variation in the CYP1A1 gene.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10 -4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.

97 MATHEMATICS AND COMPUTING↗