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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of F and B Operation Strategy with LPSI and H-SIT

In the current nuclear power plants (NPPs), most of the critical safety functions are provided by many active safety systems. However, in order to meet the futuristic goals of safety, relying on these active safety systems alone does not seem to be viable. As passive system has a strength on the mitigation of station black out accident and it also contributes to increase the diversity of mitigation way, application of passive system becomes a paramount in the nuclear industry to enhance the nuclear plant safety. Recently, many researches for developing passive systems and operation strategies have been carried out in the nuclear field. General passive systems and strategies, however, have a critical limitation, especially for passive injection systems. As these systems use gravity as their driving force, it is difficult to inject coolant in a passive way in a high-pressure condition thus depressurization has to be preferentially performed to do passive injection. The problem is that depressurization inevitably causes the loss of core inventory thus it is not preferred in order to secure plant safety. Hybrid safety injection tank (H-SIT) system was newly suggested a few years ago to passively inject the coolant into the reactor coolant system (RCS) without depressurization in a high-pressure condition. The H-SIT system is planned to be integrated into advanced power reactor plus (APR+). Generally, the H-SIT system can inject water using the pressure from nitrogen gas as a conventional safety-injection tank in low-pressure accidents, such as large and medium-break loss of coolant accidents (LOCA). Additionally, the H-SIT system can also inject water using gravitational force in high-pressure accidents. If a high pressure accident occurs, the pressure of each H-SIT is equalized with pressure of pressurizer through equalizing pipes, thus allowing the H-SITs to inject water by gravitational force. In the target plant, four H-SITs are planned to be installed. Each has a 2.5 in diameter equalizing pipe and it can be operated manually. Long-term cooling of core is an ultimate goal of all mitigation actions for plant safety and feed and bleed (F and B) operation strategy is one of the long-term cooling strategies in conventional pressurized water reactor (PWR). The important point of F and B operation is that injection for feed operation is performed by only high pressure safety injection (HPSI) pump because operation of low pressure safety injection (LPSI) pump needs the depressurization and it causes core dry out. As H-SIT is passively operated in an accident situation, it was originally designed for mitigation of station black out accident. Besides, operator can use the H-SIT system same as high pressure injection pump in any accident situations. Thus, function of high pressure injection by H-SIT can be used for feed operation when HPSI pumps fail. If it is possible, H-SIT can make up the core during depressurization then LPSI pumps can be used for F and B operation. In this study, F and B operation strategy with LPSI and HSIT is developed. This is a new concept for the long-term cooling operation. If this strategy is applied, low pressure injection pump can be successfully used for F and B operation thus operator has the additional mitigation way. As this strategy make plant safe even though HPSI and PAFS are both failed, it can effectively enhance the plant safety. For this strategy two RCGVSs and two POSRVs are needed as a depressurization system for bleed operation and only one LPSI is enough for feed operation. H-SIT operation is also needed to make up core inventory during bleed operation. For this operation, four H-SITs have to be used to make up core safely. Based on the risk analysis using PSA method, if this strategy is applied, core damage frequency is 1.868 e-6 which declined 7 percent from original model. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.

Mathematics and Computing↗
DOE OSTI2012

Enhanced High Temperature Performance of NOx Storage/Reduction (NSR) Materials

This annual report describes progress on a CRADA project aimed at developing a fundamental understanding of candidate next generation NSR materials for NOx after-treatment for light-duty lean-burn (including diesel) engines. Model catalysts that are based on literature formulations are the focus of the work being carried out at PNNL. In addition, the performance and stability of a realistic high temperature NSR catalyst, supplied by JM, is being studied in order to provide baseline data for the model catalysts that are, again, based on formulations described in the open literature.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Activity Trends for Catalytic CO and NO Co-Oxidation at Low Temperature Diesel Emission Conditions

The diesel oxidation catalyst (DOC) is an essential component of modern vehicle emissions control systems. The pervasive challenge for low temperature oxidation of engine exhaust gas is the mutual inhibition between the various pollutants, causing a marked increase in light-off temperature. Using a combination of density functional theory and descriptor-based microkinetic modeling, we have screened catalysts for low temperature co-oxidation of CO and NO with specific emphasis on minimizing inhibition effects. Compared to standard Pt–Pd alloys, we find that coinage metal alloys, i.e., Cu, Ag, and Au with at least one oxophilic constituent, should possess more robust low temperature activity with minimal inhibition. We attribute this remarkable performance to high surface concentrations of oxygen due to the oxophilic component and less competitive adsorption between CO and NO to the exposed coinage metal sites. We believe that these fundamental insights provide valuable design principles for improved low temperature oxidation catalysts.

02 PETROLEUM↗
DOE OSTI2017

Image-based pupil plane characterization via a space-domain basis

© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE). Aberration characterization plays a critical role in the development of any optical system. State-of-the-art lithography systems have the tightest aberration tolerances. We present an approach to image-based pupil plane amplitude and phase characterization using models built with a space-domain basis, in which aberration effects are separable. A polynomial model is constructed between the projections of the image intensity for chosen binary mask targets onto this basis and pupil amplitude or phase variation. This method separates model building and pupil characterization into two distinct steps, thus enabling rapid pupil characterization following data collection. The basis is related to both the transmission cross-coefficient function and the principal components of the image intensity. The pupil plane variation of a zone-plate lens from the Semiconductor High-NA Actinic Reticle Review Project (SHARP) at Lawrence Berkeley National Laboratory is examined using this method. Results are compared to pupil plane characterization using a previously proposed methodology where inverse solutions are obtained through an iterative process involving least-squares regression.

Levinson, Zac↗
DOE OSTI2019

Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era

Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production. In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R2 values more than 0.7 and 0.9, respectively. This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.

09 BIOMASS FUELS↗
DOE OSTI2014

Validation of Heat Transfer Thermal Decomposition and Container Pressurization of Polyurethane Foam.

Polymer foam encapsulants provide mechanical, electrical, and thermal isolation in engineered systems. In fire environments, gas pressure from thermal decomposition of polymers can cause mechanical failure of sealed systems. In this work, a detailed uncertainty quantification study of PMDI-based polyurethane foam is presented to assess the validity of the computational model. Both experimental measurement uncertainty and model prediction uncertainty are examined and compared. Both the mean value method and Latin hypercube sampling approach are used to propagate the uncertainty through the model. In addition to comparing computational and experimental results, the importance of each input parameter on the simulation result is also investigated. These results show that further development in the physics model of the foam and appropriate associated material testing are necessary to improve model accuracy.

Scott, Sarah Nicole↗
DOE OSTI2022

Temperature Controlled Glass Additive Manufacturing

Glass additive manufacturing (AM) is an emerging technology with a wide range of applications in chemistry, biology, optical science, and engineering. A custom laser-based glass 3D printer was developed by a group of engineers at Los Alamos National Laboratory (LANL) and is being optimized to produce hardware for chemical processing equipment, microfluidic systems, as well as optical elements. AM glass using a filament deposition modeling (FDM) approach allows for generation of parts with high optical quality. The quality of the glass can be affected by the generation of defects such as bubbles and vaporization which affect the optical quality of the glass. The defects can also affect the mechanical performance of the glass and lead to sample cracking.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Knowledge-matching based computational framework for genome-scale metabolic model refinement

Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Materials engineering tetrahedron economic evaluation tool

A new open-source project evaluation tool, entitle the Materials Engineering Tetrahedron (MET), has been developed to determine the economic viability of materials design, selection, processing, and validation costs associated with any infrastructure-based project. MET improves project design by providing an economic perspective to the traditional materials science tetrahedron, relating microstructure, processing, property, and performance through the introduction of value-based economic costs for each side of the tetrahedron. The resulting size and distortion, from a regular tetrahedron illustrates the balance between the system, component, or material fabrication project detailed. Furthermore, the MET model also allows for increased budget efficiency and the potential for improved identification of cost saving mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Parametric analysis and optimization of CO 2 trans-critical cycle for chiller application in a warm climate

This article presents parametric analysis and subsequent optimization of a CO 2 trans-critical chiller system operating in a warm climate. Additionally, high side pressure and gas cooler face velocities are two controllable parameters investigated. COP of the system is analyzed and optimized using a developed and validated mathematical model. The mean relative error of prediction in COP is found to be within ±10% for physics-based model and within ±1% for Artificial Neural Network (ANN) based model of the experimental findings. The validated mathematical model is utilized to predict optimal high side pressure as well as gas cooler face velocity for the varying ambient and evaporation conditions to achieve best possible COP. A possibility of 5.31% improvement in COP is found based on the optimization of parameters. In conclusion, the proposed methodology is deemed suitable for design and testing of control system for maximization of energy efficiency.

42 ENGINEERING↗
DOE OSTI2018

Cyberattack Detection for Cyber Physical Systems Security–A Preliminary Study.

Cyber-physical systems (CPS) security has become an increasingly important research topic in recent years. Geared towards more advanced cyberattack detection techniques as part of strategies for enhancing the security of CPS, in this paper we propose a machine learning based cyber-attack detection scheme. The proposed scheme is a physical-domain technique; specifically, it assumes the physical measurements of the system carry sufficient information for capturing the system behavior, thus can be used for differentiating normal operation and attacks. CPS are complex in nature and the number of physical measurements available for CPS is often overwhelmingly high. Thus, accurately modeling CPS’ dynamic behavior, more importantly, distinguishing normal and adversary activities based on the large number of physical measurements, can be challenging. To address the challenge, we have focused our research effort on feature engineering, that is, to intelligently derive a set of salient signatures or features from the noisy measurements. We make sure the derived features are more compact and, more importantly, have more discriminant power than the original physical measurements, thus enabling us to achieve more accurate and robust detection performance. To demonstrate the effectiveness of the proposed scheme, in our experimental study we consider gas turbines of combined cycle power plants as the cyberphysical system. Using the data from the high-fidelity simulation we show that our proposed cyberattack detection scheme is able to achieve high detection performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Development of one-dimensional computational fluid dynamics code 'GFLOW' for groundwater flow and contaminant transport analysis

Prediction of groundwater movement and contaminant transport in soil is an important problem in many branches of science and engineering. This includes groundwater hydrology, environmental engineering, soil science, agricultural engineering and also nuclear engineering. Specifically, in nuclear engineering it is applicable in the design of spent fuel storage pools and waste management sites in the nuclear power plants. Ground water modeling involves the simulation of flow and contaminant transport by groundwater flow. In the context of contaminated soil and groundwater system, numerical simulations are typically used to demonstrate compliance with regulatory standard. A one-dimensional Computational Fluid Dynamics code GFLOW had been developed based on the Finite Difference Method for simulating groundwater flow and contaminant transport through saturated and unsaturated soil. The code is validated with the analytical model and the benchmarking cases available in the literature. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Development of Membrane Distillation Technology Utilizing Waste Heat for Treatment of High Salinity Wastewaters

Membrane distillation (MD) can serve as a cost effective method to treat high salinity wastewaters, especially if waste heat is utilized for its operation. The goal of this work is to study the feasibility of employing MD technology for the treatment of high salinity wastewaters which are generated as a result of natural gas production and active reservoir management for CO 2 sequestration. This study also aimed at quantifying the availability of waste heat which could be used as the primary source of thermal energy to drive the MD process. Bench scale studies were carried out with several commercially available membranes using both synthetic and actual high salinity wastewaters. The membranes tested with real wastewater samples exhibited excellent rejection of dissolved ions, including naturally occurring radioactive material (NORM). Long term bench scale experiments with real produced water from the Marcellus shale play revealed that iron oxide could precipitate on the feed side of direct contact MD (DCMD) module but had a negligible effect on the performance of the system. Based on the bench scale experiments, an ASPEN Plus simulation, which incorporated a mathematical model with fundamental heat and mass transfer equations, was developed and used to evaluate the process energy requirements. Despite low operating temperature and pressure for MD, it had a large thermal energy requirement for operation, which could make this technology expensive compared to reverse osmosis (RO) and forward osmosis (FO). However, it is important to note that RO and FO have limited applicability for high salinity shale gas wastewater. Moreover, MD could be integrated with renewable energy sources such as wind or solar energy or industrial waste heat sources to offset the thermal energy requirements and enhance the economics of wastewater treatment. Natural gas compressor stations (NG CS) have been proposed in this study as the source of waste heat based on the estimate of quantity and quality of available waste heat in the exhaust streams of gas turbine and internal combustion compressor engines. Using the actual installed capacity and location of compressor stations in the U.S., this analysis revealed that an average of 610 TJ/day of thermal energy is available at existing NG CS and that this source of waste heat is of high quality with temperatures above 645 K. A systems-level integration of MD with waste heat from NG CS revealed that approximately 56% of available waste heat at NG CS in Pennsylvania is sufficient to concentrate (from 10% to 30% salinity) all wastewater associated with shale gas extraction. Techno-economic assessment (TEA) accounting for capital as well as operating and maintenance costs with and without waste heat integration was conducted for a hypothetical 0.5 million gallons per day (MGD) DCMD plant using experimental and modeling results from this study. TEA revealed that the total cost of shale gas wastewater treatment using MD is $5.7/m 3 feed for the base case scenario in which the thermal energy requirements are met by external steam, however, the total cost can be reduced to $0.74/m 3 feed when integrated with free waste heat from NG CS. Sensitivity analysis revealed that thermal energy cost and feed TDS are the most important factors in total cost of high salinity wastewater treatment using DCMD.

03 NATURAL GAS↗
DOE OSTI2018

The Building ENergy Demand (BEND) Model: A Comprehensive Composite Building Energy Modeling Framework

The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Residential Real-time Price Response Simulation

The electric industry is gaining experience with innovative price responsive demand pilots and limited roll-outs to customers. One of these pilots is investigating real-time pricing signals to engage end-use systems and local distributed generation and storage in a distributed optimization process. Attractive aspects about the approach include strong scalability characteristics, simplified interfaces between automation devices, and the adaptability to integrate a wide variety of devices and systems. Experience in this nascent field is revealing a rich array of for engineering decisions and the application of complexity theory. To test the decisions, computer simulations are used to reveal insights about design, demand elasticity, and the limits of response (including consumer fatigue). Agent-based approaches lend themselves well in the simulation to modeling the participation and interaction of each piece of equipment on a distribution feeder. This paper discusses rate design and simulation experiences at the distribution feeder level where consumers and their HVAC systems and water heaters on a feeder receive real-time pricing signals.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Energize: An Interactive Evaluation Tool for Engaging the General Public with Energy Decision Making - Final Report

The public has an ever-increasing interest in the economic, environmental, and social impacts of global energy production. To support informed decision making, the scientific community has a responsibility to communicate reliable and straight-forward information to the general public, in an engaging way, regarding energy systems and how choices made at different stages of an energy technology life cycle can impact the cost, amount of materials used, and waste produced. Our objective is to enhance public engagement via an interactive electrical energy game (Energ!ze) through which players interact with one another in their quest to develop an electrical energy portfolio that optimizes economic (e.g., company profit), environmental (e.g., reduced CO 2 emissions), and social (e.g., public opinion) impacts. The design and development of Energ!ze was an iterative process involving an interdisciplinary team of engineers, discipline-based scientists, computer scientists, and education experts. The development objectives were two-fold: (1) Accurate and quantifiable modeling of electrical energy systems and (2) Engaging and interactive user interface within the construct of the game narrative. The major game mechanics in the current playable Energ!ze version focus on economic metrics, and the database is scaffolded to add environmental and social metrics for future versions. This report details the outcome of the funding period (Section 2 and 3) and describes the development process, along with recommendations for future development (Section 4).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗