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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Heavy Duty Natural Gas Single Cylinder Research Engine Installation, Commissioning, and Baseline Testing

Natural Gas (NG) Internal Combustion Engines (ICE) are a promising alternative to diesel engines for on-road heavy-duty applications to reduce greenhouse gas and harmful pollutant emissions. NG engines have not been widely adopted due to the lower thermal efficiency compared with diesel engine counterparts. To develop the base knowledge required to reach the desired efficiency, a Single Cylinder Engine (SCE) is the most effective platform to acquire reliable and repeatable data. A SCE test cell was developed using a Cummins 15-liter six-cylinder heavy-duty engine block modified to fire one cylinder (2.5-liter displacement). A Woodward Large Engine Control Module (LECM) is integrated to permit implementation of real-time advanced combustion control. Intake and exhaust characteristics, fuel composition, and exhaust gas recirculated substitution rate (EGR) are fully adjustable. A high-speed data acquisition system acquires in-cylinder, intake, and exhaust pressure for combustion analysis. The baseline testing shows reliable and consistent results for engine thermal efficiency, indicated mean effective pressure (IMEP), and coefficient of variance of the IMEP over a wide range of operating conditions while achieving effective control of all engine control and operation variables. This test cell will be used to conduct a research program to develop new and innovative control algorithms and CFD optimized combustion chamber designs, allowing ultra-high efficiency and low emissions for NG ICE heavy-duty on-road applications.

03 NATURAL GAS↗
DOE OSTI2020

Appropriations and Financing: Strategic Use of Funding for Performance Contracts

42 U.S.C. § 8253 Sections 432 and 512 (EISA of 2007) authorize agencies to use appropriations, private financing, or a combination of appropriations and private financing to comply with its requirements. With this flexibility in mind, federal agencies should evaluate funding options early and employ a strategy that optimizes the impact of existing appropriations and financing options. Maximize value to government through strategic use of appropriations and energy savings, an optimal set of infrastructure improvements that meet site needs and program objectives, and an actionable plan for sustained performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Integration of Dynamical System Scaling to RAVEN and Facility Application

As part of the design optimization and model validation effort within the hybrid energy systems program, the research to implement dynamical system scaling (DSS) code to RAVEN is funded under the Integrated Energy System program in collaboration with the Digital Reactor Technology & Development department. The DSS data processing algorithm has been coded within the RAVEN framework along with other metrics and postprocessing models. The implemented code was tested using a gravity-driven draining tank draining case modeled in RELAP5-3D and generated data was successfully postprocessed based on the scaling analyses defined by DSS. Postprocessed data concluded less agreement for the tank exit velocity and indicated that the modeled input deck for RELAP5-3D requires modification to enforce minimal pressure differential effects to the draining process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Development of High Strength HEPA Filters for Use in Demanding Air and Gas Streams - 17644

High-efficiency particulate air (HEPA) filters are used in a wide variety of applications, including the removal of radioactive particulate from process off-gas streams and the venting of waste storage containers. Porvair Filtration Group, specializing in the design and manufacture of robust filters in numerous industries, has worked to develop HEPA filter designs for use in such high-demand nuclear process-gas and air streams. Using the requirements of section FK of the American Society of Mechanical Engineers AG-1 Code on Nuclear Air and Gas Treatment (ASME AG-1) as a guide, Porvair Filtration Group has designed filters which can withstand upset conditions to include elevated temperatures, high relative humidity, high particle-loading, and an unconventionally high differential pressure across the filter media; while maintaining excellent removal efficiencies. The process by which Porvair Filtration Group has reached optimized designs was based upon an extended test and development program. Design, manufacture, and testing of flat sheet media samples, geometrically equivalent model filters, and full-scale prototypes was performed at several Porvair Filtration Group facilities located in the United States and the United Kingdom. The methods and results of this development program are outlined in the following paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Experience in environmental remediation of radioactively contaminated site in waste disposal area in Istanbul - 15011

In this paper specific environmental remediation examples of method applications are given. Radiological contaminated site was created by non-nuclear industry in Turkey. This is designated as a contaminated site and can ultimately lead to undesired health effects for residents. Appropriate actions have been taken to protect human and environment from contaminated site. As every site has its own characteristics, contaminated area was examined by sampling and surveying works. Before taken into action, remediation actions need to be justified and optimized. Radiation levels and homogeneity of contamination was investigated. Remediation program was established and taken into action in parallel two main works. First one covers all actions to the contamination itself. The site was isolated properly and all equipment was deployed. Working methods for segregation of contaminated material from the soil were determined. Besides, risk evaluation was carried out. Pathways between the contaminated soil and people were taken into account. Groundwater samples were taken and analyzed. According to these results, evacuation, area isolation or changing land use options were evaluated. Isolation of the site and removal of radioactive materials were determined as appropriate actions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

An Unsteady State Gas Transport Model in Matrix Laboratory (MATLAB) for PWR Gas Management

Reactor Coolant System (RCS) hydrogen gas management can be a challenge at Pressurized Water Reactors (PWRs) during shutdown, startup, and steady state operation. Poor hydrogen control can result in higher general corrosion rates for RCS components and increases in critical path time for shutdown or startup. Also, performance indicator penalties may be accrued by entry into Electric Power Research Institute (EPRI) Action Level conditions or by accrual of Chemistry Effectiveness Index (CEI) penalties established by the Institute of Nuclear Power Operations (INPO). Furthermore, poor hydrogen control may increase industrial safety risk due to the creation of an explosive environment. Part of the hydrogen management difficulty arises from the interaction of several phenomena that require complex simulations to fully understand. While hydrogen gas transport models have been developed, the application of these models is limited by the computational intensity required to accurately model the dynamism inherent in plant shutdown or startup. Subsequently, a gas transport model is developed in a suitable computational program to meet these needs and provide input to an optimized RCS hydrogen management strategy for all operational modes. The gas transport model is used to model RCS hydrogen behavior for an example PWR during an End of Cycle (EOC) Forced Outage (FO) startup, a refueling outage (RFO) shutdown, and during nominal steady state operation. The computational software selected is Matrix Laboratory (MATLAB{sup R})

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Design and Demonstration of a NH3-Fueled Two-Stroke Uniflow Engine for Greenhouse Gas Reduction

The maritime shipping industry is growing increasingly interested in both low and non-carbon-containing fuels to meet future greenhouse gas emission targets. Specifically of interest is ammonia, as it has a relatively high volumetric energy density compared to other future fuels, such as hydrogen, making it more economical to transport. The robust engine architecture of low-speed two-stroke marine engines makes them an ideal candidate for ammonia fuel, overcoming many of the issues surrounding its poor ignitability and low flame speed. If emissions and fueling system challenges can be addressed, retrofits of current low-speed two-stroke dual-fuel engines represent a viable pathway for bringing ammonia engines to market. This study explores these technical hurdles by describing the design, analysis, and experimental validation of a single cylinder research engine converted to operate on ammonia fuel. The engine is a reduced-scale uniflow two-stroke marine engine with two previous hardware configurations available – diesel and high-pressure CNG dual-fuel. A concept study was used to evaluate possible ammonia-fueled engine architectures and the associated tradeoffs and design considerations. With the chosen architecture, low-pressure dual fuel, 1D and 3D analysis tools were used to inform hardware selection and to determine hardware configurations which minimized ammonia-slip. In addition to these considerations the hardware and engine configuration were designed to provide a versatile and robust testing platform. This includes options to test both gaseous and liquid ammonia injection, as well as a wide range of performance parameters such as AFR, swirl, valve timing, SOI, and many others. Design constraints imposed by the existing engine hardware necessitated an iterative loop between design and analysis toolsets, ultimately converging on a final design for the ammonia-conversion hardware. The engine was rebuilt with the new hardware and evaluated in an engine test cell. A new control strategy developed and flashed onto a prototyping electronic control unit allowed for full control over all engine parameters. An initial calibration was developed, providing test data for validation of the engine 1D and 3D models. The impact of the design choices on engine operability and the ability to meet program targets is discussed as well as opportunities for further optimization of the ammonia-conversion hardware, informed by the validated models.

Kaul, Brian [ORNL] (ORCID:0000000184813620)↗
DOE OSTI2017

A Vision for Co-optimized T&D System Interaction with Renewables and Demand Response

The evolution of the power system to the reliable, effi- cient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. The use of demand response to counterbalance the intermittency of re- newable generation brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distri- bution system, these resources are typically modeled as variables at the transmission network level. In this paper, a vision for co- optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system is described. In this frame- work, microgrids encompass consumers, distributed renewables and storage. The energy management system of the microgrid can also sell (buy) excess (necessary) energy from the transmission system. Preliminary work explores price mechanisms to manage the microgrid and its interactions with the transmission system. Wholesale market operations are addressed through the devel- opment of scalable stochastic optimization methods that provide the ability to co-optimize interactions between the transmission and distribution systems. Modeling challenges of the co-optimization are addressed via solution methods for large-scale stochastic op- timization, including decomposition and stochastic dual dynamic programming.

14 SOLAR ENERGY↗
DOE OSTI2021

Design and dispatch optimization of packaged ice storage systems within a connected community

The traditional implementation of cool thermal energy storage (CTES) must be reimagined within the context of a dynamic grid and smart buildings operating as connected communities. As most buildings do not operate central chillers or connect to district cooling loops, this necessitates a broader use of packaged CTES. Our objective is to begin answering the question of how such packaged CTES should be implemented within a connected community. We do so by presenting a simulation–optimization workflow employing building energy modeling software and a mixed-integer linear program to design and dispatch a packaged CTES technology to achieve minimum total annual cost. We demonstrate this methodology on a seven-building case study using current utility rates and find that total annual cooling energy costs can be reduced by 17.8% compared to baseline, after accounting for the cost of storage. We perform three parametric sensitivity studies to evaluate modeling assumptions and obtain the prioritization of storage procurement as a function of annualized life-cycle cost of storage. We find that a community optimization approach provides significantly different results than individual building optimizations and provides greater savings compared to baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Corporate Delivery of a Global Smart Buildings Program

Buildings account for about 40 percent of the total energy consumption in the U.S. and emit approximately one third of greenhouse gas emissions. But they also offer tremendous potential for achieving significant greenhouse gas reductions with the right savings strategies. With an increasing amount of data from buildings and advanced computational and analytical abilities, buildings can be made “smart” to optimize energy consumption and occupant comfort. Smart buildings are often characterized as having a high degree of data and system integration, connectivity and control, as well as the advanced use of data analytics. These “smarts” can enable up to 10–20% savings in a building, and help ensure that they persist over time. In 2009, Microsoft Corporation launched the Energy-Smart Buildings (ESB) program with a vision to improve building operations services, security and accessibility in services, and new tenant applications and services that improve productivity and optimize energy use. The ESB program focused on fault diagnostics, advanced analytics and new organizational processes and practices to support their operational integration. In addition to the ESB program, Microsoft undertook capital improvement projects that made effective use of a utility incentive program and lab consolidations over the same duration. The ESB program began with a pilot at Microsoft's Puget Sound campus that identified significant savings of up to 6–10% in the 13 pilot buildings. The success of the pilot led to a global deployment of the program. Between 2009 and 2015, there was a 23.7% reduction in annual electricity consumption (kWh) at the Puget Sound campus with 18.5% of that resulting from the ESB and lab consolidations. This article provides the results of research conducted to assess the best-practice strategies that Microsoft implemented to achieve these savings, including the fault diagnostic routines that are the foundation of the ESB program and organizational change management practices. It also presents the process that was adopted to scale the ESB program globally. We conclude with recommendations for how these successes can be generalized and replicated by other corporate enterprises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Scenario generation for stochastic optimization problems via the sparse grid method

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid method can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2015

Optimal Voltage Regulation for Unbalanced Distribution Networks Considering Distributed Energy Resources

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative quadratic constrained quadratic programming model to minimize voltage deviations and maximize distributed energy resource (DER) active power output in a three phase unbalanced distribution system is developed. The optimization model is based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DERs). To avoid the oscillation of solution when it is close to the optimum, a golden search method is introduced to control the step size. Numerical simulations on modified IEEE 13 nodes test feeders show the efficiency of the proposed model. Compared to the results solved by heuristic search (harmony algorithm), the proposed model converges quickly to the global optimum.

distributed energy resources↗
DOE OSTI2014

TAS: 89 0227: TAS Recovery Act - Optimization and Control of Electric Power Systems: ARRA

The name SuperOPF is used to refer several projects, problem formulations and soft-ware tools intended to extend, improve and re-define some of the standard methods of optimizing electric power systems. Our work included applying primal-dual interior point methods to standard AC optimal power flow problems of large size, as well as extensions of this problem to include co-optimization of multiple scenarios. The original SuperOPF problem formulation was based on co-optimizing a base scenario along with multiple post-contingency scenarios, where all AC power flow models and constraints are enforced for each, to find optimal energy contracts, endogenously determined locational reserves and appropriate nodal energy prices for a single period optimal power flow problem with uncertainty. This led to example non-linear programming problems on the order of 1 million constraints and half a million variables. The second generation SuperOPF formulation extends this by adding multiple periods and multiple base scenarios per period. It also incorporates additional variables and constraints to model load following reserves, ramping costs, and storage resources. A third generation of the multi-period SuperOPF, adds both integer variables and a receding horizon framework in which the problem type is more challenging (mixed integer), the size is even larger, and it must be solved more frequently, pushing the limits of currently available algorithms and solvers. The consideration of transient stability constraints in optimal power flow (OPF) problems has become increasingly important in modern power systems. Transient stability constrained OPF (TSCOPF) is a nonlinear optimization problem subject to a set of algebraic and differential equations. Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability in OPF. To handle the dynamics in TSCOPF, the set of differential equations can be approximated or converted into equivalent algebraic equations before they are included in an OPF formulation. In Chapter 4, a rigorous evaluation of using a predefined and fixed threshold for rotor angles as a mean to determine transient stability of the system is developed. TSCOPF can be modeled as a large-scale nonlinear programming problem including the constraints of differential-algebraic equations (DAE). Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability constraint in OPF. Unfortunately, even the current best TSCOPF solvers still suffer from the curse of dimensionality and unacceptable computational time, especially for large-scale power systems with multiple contingencies. In chapter 5, thse issues will be addressed and a new method to incorporate the transient stability constraints will be presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Limits of multifunctionality in tunable networks

Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single “target” link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of constraint–satisfaction problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

IEA Wind Energy Task 37 - System Engineering - Aerodynamic Optimization Case Study

This paper presents the results from the first IEA Wind Task 37 aerodynamic optimization case study. 8 participants applied their optimization tools to a purely aerodynamic problem and the results were compared. Overall, the different tools produced widely different designs, while there was better agreement in the improvement achieved. This highlights the fact that further investigation is needed to try to understand these differences and develop best practices. There were too many differences between the different analysis and optimization codes to determine the sources of these differences. However, several potential sources of discrepancy were identified for further investigation. One hypothesis is that one potential source of discrepancy is that the design problem itself is relatively flat in design directions of constant loading. This flatness would impact the convergence in the optimization, while at the same time mean that discrepancies in the design itself would be less severe.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

IEA Wind Energy Task 37 System Engineering Aerodynamic Optimization Case Study: Preprint

This paper presents the results from the first IEA Wind Task 37 aerodynamic optimization case study. Eight participants applied their optimization tools to a purely aerodynamic problem and the results were compared. Overall, the different tools produced widely different designs, while there was better agreement in the improvement achieved. This highlights the fact that further investigation is needed to try to understand these differences and develop best practices. There were too many differences between the different analysis and optimization codes to determine the sources of these differences. However, several potential sources of discrepancy were identified for further investigation. One hypothesis is that one potential source of discrepancy is that the design problem itself is relatively flat in design directions of constant loading. This flatness would impact the convergence in the optimization, and at the same time mean that discrepancies in the design itself would be less severe.

49 EE - Wind and Water Power Program - Wind (EE-4W↗