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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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This fact sheet explores how to correctly map building loads to rooftop unit (RTU) capacity to help minimize capital investment and ongoing operational costs while maximizing thermal comfort.
The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.
In this case study, we will describe the design and assembly of a cyber security test range we have built at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide a flexible environment to evaluate cyber security tools—particularly those involving AI/ML—in a way that provides realistic environments and where we can control the experiments to determine the strengths and weaknesses of the tools. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized environments. These evaluations will be described, in order to provide motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K malware samples chosen across a range of types. The second is network detection of attempted penetration and exploitations with varying levels of covertness in a high-volume, business network. The scale of each of these challenges is requiring us to create automation systems to repeat the experiments identically for each tool. Preventing there being easy signs of malicious activity for the AI/ML tools to focus on has been a particularly interesting and challenging aspect of designing and executing these challenge events. After the events, the range continues to be used for other research such as adversarial machine learning where the repeatability, scale, and automation required for the national challenge events become essential elements for research.
This paper proposes a computationally efficient mixed integer linear programming (MILP) model for the coordinated optimization of solid-state power substations (SSPSs) in a feeder considering the full unbalanced three-phase structure of the distribution grid and its characteristics. The proposed model determines the optimal real and reactive power of each SSPS at the point of common coupling (PCC) that minimizes the operating cost and maximizes the system performance, e.g., voltage regulation and phase balancing. To improve the computational efficiency, an inscribed octagon is introduced to approximate the quadratic capacity constraints of components. Numerical simulation results show the effectiveness of the proposed model and significant improvements in voltage profiles and power imbalance between phases.
The injection well stimulation project at the Raft River geothermal field tests the effect of long-term cold water injection and high pressure injection on well injectivity, improvements to which could reduce operating costs. The primary data for analysis and interpretation of the injection test are step-rate flow tests run before each new phase of the injection. These tests were analyzed using a combination of standard pump-test analytical solution methods and methods developed expressly for the observed conditions. The stepped rate injection tests, combined with long-term flow and pressure response data suggest that the well is located within a fractured formation of low transmissivity but high storativity. These calculated parameters appeared to increase with pressure during the first injection test and the higher values were reproduced during the second stepped rate test. Calculated transmissivity and storativity are on the order of 4E-5 m cm 2 and 1E-4 m Pa -1 , respectively. The apparent pressure dependence of fitted hydraulic parameters may reflect near-well fracture compliance that increased the effective radius of the wellbore during the first test. While the type curve fit analysis also suggests that the reservoir behaves as a uniformly fractured reservoir with a radial flow regime, the hydraulic parameters indicate that condition may exist only a very limited distance (<10 m) from the well. Longer-term pressure response suggests that flow in the system effectively reaches steady state in a period of less than a day, which may reflect pressure stabilization resulting from pressure-dependent permeability or a region of much higher permeability located with a few meters of the well. The transmissivity estimates obtained from this analysis, converted to approximate fracture density and aperture, provide useful constraints on the distance to which the thermal front may migrate from the well during the cold water injection phase of the stimulation project. We estimate that the cooling front will migrate less than a tenth of a kilometer over an approximately one-year injection period. Here, the effects of that cooling, however, may be substantial, because increases in permeability have maximum effect nearest the well.
While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in some buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price, grid load, and bus status. The framework connects environmental models for traffic (SUMO) and the electric grid (GridLAB-D) to a planner, which evaluates bus charging decisions with regard to their long-term effect on grid reliability and cost. The framework has been evaluated on a simulation of Richland, WA’s bus and grid network, and compared to greedy methods.
Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.
Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.
Today’s connected lighting systems have the potential to reduce energy consumption and operational costs via the use of the data they collect and share with other building systems (e.g., HVAC, building automation, security). However, many market available products are new to being networked, and when networked components in lighting and other building systems are not sufficiently secured, they present opportunities for criminals to exploit. Further, security vulnerabilities in one system can be used as lateral steppingstones that allow access to other prized assets on the same network. These cybersecurity concerns could deter the adoption and use of connected systems, which then could jeopardize long-term national objectives for reduced energy usage. The workflows described here and presented in more detail in the referenced reports are examples of how these frameworks and tools can be put to practical use during system design and specification.
This report provides a summary of the development effort for the qualification of Alloy 709, an advanced austenitic stainless steel, in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors. It provides an assessment of the mechanical properties data generated to date from the tensile, creep, fatigue and creep-fatigue tests of the precipitation-treated Alloy 709 from two commercial heats in plate product form. It was concluded that the mechanical properties of Alloy 709 with the precipitation treatment continued to outperform those of Type 316 stainless steel. This affirms the recommendation to Code qualification this alloy as a replacement for Type 316 stainless steel to support the objective of reducing the construction and operating costs to incentivize advanced reactor deployment. The data also demonstrated that the precipitation treatment is effective in enhancing the creep-fatigue resistance of Alloy 709 while maintaining a significant creep strength advantage over Type 316 stainless steel. This report also provides an update to the test conditions for the creep, fatigue and creep-fatigue test matrices in order to cover the Code Case data package more effectively. Finally, it is recommended to continue the Alloy 709 Code Case Testing Program to develop the data package needed for the determination of the material-specific design parameters for inclusion in the Alloy 709 Code Case.
According to the U.S. Department of Energy Combined Heat and Power Installation Database, approximately 80 GW of electrical power is produced in the United States using combined heat and power (CHP) systems. Studies have also shown that a potential market for CHP applications is larger than the existing market. Since fuel consumption is the most significant part of the operating cost for a CHP plant, higher efficiency translates to increased power output for the same quantity of fuel.
District energy systems efficiently provide thermal energy to multiple buildings and facilities through a network of shared infrastructure. Frequently, district energy systems are centered around combined heat and power (CHP) plants that generate electricity as well as heating and cooling to the local buildings. By providing both electricity and thermal energy from a single fuel source and central location, these systems use less fuel, decrease energy and operational costs, and reduce the need for heating and cooling equipment in individual buildings.
District energy systems efficiently provide thermal energy to multiple buildings and facilities through a network of shared infrastructure. Frequently, district energy systems are centered around combined heat and power (CHP) plants that generate electricity, as well as heating and cooling, to the local buildings. By providing both electricity and thermal energy from a single fuel source and central location, these systems use less fuel, decrease energy and operational costs, and reduce the need for heating and cooling equipment in individual buildings.
According to the U.S. Department of Energy Combined Heat and Power Installation Database, approximately 80 GW of electrical power is produced in the United States using combined heat and power (CHP) systems. Studies have also shown that a potential market for CHP applications is larger than the existing market. Since fuel consumption is the most significant part of the operating cost for a CHP plant, higher efficiency translates to increased power output for the same quantity of fuel.
District energy systems efficiently provide thermal energy to multiple buildings and facilities through a network of shared infrastructure. Frequently, district energy systems are centered around combined heat and power (CHP) plants that generate electricity as well as heating and cooling to the local buildings. By providing both electricity and thermal energy from a single fuel source and central location, these systems use less fuel, decrease energy and operational costs, and reduce the need for heating and cooling equipment in individual buildings.
Electric vehicles (EVs) use energy more efficiently than gasoline vehicles. This is one of their primary attributes, enabling other benefits such as improved torque and reduced operating costs and greenhouse gas emissions. An electric vehicle efficiency ratio (EVER) is therefore important when calculating the financial and environmental benefits of EVs, calculating the impact that EVs have on a manufacturer's Corporate Average Fuel Economy rating, calculating credits in trading schemes such as California's Low Carbon Fuel Standard, fuel price leveling, designing electricity tariffs from utility-owned public charging infrastructure, creating EV alternatives to gasoline excise taxes, and more.
Describes the role of the ATLAS Continuous Integration (CI) System in the ATLAS offline software development infrastructure • Outlines the CI system components and processes • Details Operational Intelligence techniques to accelerate CI jobs and lower operating costs • Explains the Directed Acyclic Graph (DAG) approach in CI pipelines • Reports achieved improvements
We present the results of commissioning a dedicated furnace for Nb3Sn coatings of 2.6 GHz single cell cavities. Nb3Sn is a desired coating due to its high critical temperature and smaller surface resistance compared to bulk Nb. Usage of Nb3Sn coated cavities will greatly reduce operating costs due to decreased dependance on cryo cooling. Tin is deposited by use of a tin chloride nucleation agent and tin vapor diffusion. Analysis of the resultant coating was performed using SEM/EDS to verify successful formation of Nb3Sn. Witness samples in line of sight of the source were used in order to understand the coating efficacy.