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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Superconducting Magnet Qualification Methodology for the Material Plasma Exposure Experiment

The Material Plasma Exposure eXperiment (MPEX), currently under design, is a new linear plasma device to advance the understanding of plasma-material interactions through the generation and delivery of plasmas as they are expected in future fusion reactor divertors. MPEX will be a steady-state device to study high-fluence exposures of plasma-facing materials and components. The requirements for the magnetic field at the target and the heating stages make the application of superconducting coils necessary. Conceptual designs for the superconducting magnets have been developed, and multiple cryostats with warm bore diameters of either 65 cm or 156 cm are envisioned to facilitate their integrated and timely assembly with other systems such as vacuum, water cooling, and RF power. Although design, fabrication, and testing for the magnets as stand-alone units are straightforward, challenges will arise during the integration of the system. Two different field profiles will be used during operation. The magnetic field where the electron cyclotron heating occurs needs to operate at both 1.25 and 2.5 T. It is critical that the magnets all share the same magnetic axis and alignment. The mutual inductance between cryostats will affect the quench behavior of the system. Also, cryostat-to-cryostat forces can be as large as 700 kN, and the magnitude and direction will change depending on which coils are energized. The design of the system must take those characteristics into account along with the quench scenarios. This paper describes the qualification approach that will be used to determine whether stand-alone tests can be used to ensure the success of the integrated system. Fiducials will be used to define the location of the magnetic axis for each cryostat to ensure proper alignment. Quench tests of a single magnet will be performed at a current above the normal operating current to account for additional stored energy from the mutual inductance to adjacent cryostats. Also, a 1018 steel plate will be mounted on either end of a cryostat to simulate the cryostat-to-cryostat forces. Requirements for the size and location of the steel plates are described.

Burkhardt, Earle↗
DOE OSTI2017

Methodology for Wide Band-Gap Device Dynamic Characterization

Here, the double pulse test (DPT) is a widely accepted method to evaluate the dynamic behavior of power devices. Considering the high switching-speed capability of wide band-gap devices, the test results are very sensitive to the alignment of voltage and current (V-I) measurements. Also, because of the shoot-through current induced by Cdv/dt (i.e., cross-talk), the switching losses of the nonoperating switch device in a phase-leg must be considered in addition to the operating device. This paper summarizes the key issues of the DPT, including components and layout design, measurement considerations, grounding effects, and data processing. Additionally, a practical method is proposed for phase-leg switching loss evaluation by calculating the difference between the input energy supplied by a dc capacitor and the output energy stored in a load inductor. Based on a phase-leg power module built with 1200-V/50-A SiC MOSFETs, the test results show that this method can accurately evaluate the switching loss of both the upper and lower switches by detecting only one switching current and voltage, and it is immune to V-I timing misalignment errors.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗
DOE OSTI2018

A Methodology for Quantifying Reliability Benefits From Improved Solar Power Forecasting in Multi-Timescale Power System Operations

Solar power forecasting improvements are mainly evaluated by statistical and economic metrics, and the practical reliability benefits of these forecasting enhancements have not yet been well quantified. This paper aims to quantify reliability benefits from solar power forecasting improvements. To systematically analyze the relationship between solar power forecasting improvements and reliability performance in power system operations, an expected synthetic reliability (ESR) metric is proposed to integrate state-of-the-art independent reliability metrics. The absolute value and standard deviation of area control errors (ACE), and the North American Electric Reliability Corporation Control Performance Standard 2 (CPS2) score are calculated through the multi-timescale scheduling simulation, including the day-ahead unit commitment (DU), real-time unit commitment (RU), real-time economic dispatch (RE), automatic generation control (AGC) sub-models. The absolute area control error in energy (AACEE), CPS2 violations, CPS2 score, and standard deviation of the raw ACE are all calculated and combined as the ESR metric. Numerical simulations show that the reliability benefits of multi-timescale power system operations are significantly increased due to the improved solar power forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

17 WIND ENERGY↗
DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

An Optimization-Based HVAC Load and PV Disaggregation Methodology With Contextual Supervision

Here, this letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV.

Analytics↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2016

Methodology for Estimating Thermal and Neutron Embrittlement of Austenitic Stainless Steel Welds During Service in Light Water Reactors

The effect of thermal aging on the degradation of fracture toughness and Charpy-impact properties of austenitic stainless steel (SS) welds has been characterized at reactor temperatures. The solidification behavior and the distribution and morphology of the ferrite phase in SS welds are described. Thermal aging of the welds results in moderate decreases in Charpy-impact strength and fracture toughness. The upper-shelf Charpy-impact energy of aged welds decreases by 50–80 J/cm2. The decrease in fracture toughness J-R curve, or JIc is relatively small. Thermal aging has minimal effect on the tensile strength. The fracture properties of SS welds are insensitive to filler metal; the welding process has a significant effect. The large variability in the data makes it difficult to establish the effect of the welding process on fracture properties of SS welds. Consequently, the approach used for evaluating thermal and neutron embrittlement of austenitic SS welds relies on establishing a lower-bound fracture toughness J-R curve for unaged and aged, and non-irradiated and irradiated, SS welds. The existing fracture toughness J-R curve data for SS welds have been reviewed and evaluated to define lower-bound J-R curve for submerged arc (SA)/shielded metal arc (SMA)/manual metal arc (MMA) welds and gas tungsten arc (GTA)/tungsten inert gas (TIG) welds in the unaged and aged conditions. At reactor temperatures, the fracture toughness of GTA/TIG welds is a factor of about 2.3 higher than that of SA/SMA/MMA welds. Thermal aging decreases the fracture toughness by about 20%. The potential combined effects of thermal and neutron embrittlement of austenitic SS welds are also described. Lower-bound curves are presented that define the change in coefficient C and exponent n of the power-law J-R curve and the JIc value for SS welds as a function of neutron dose. The potential effects of reactor coolant environment on the fracture toughness of austenitic SS welds are also discussed.

Chopra, O. K.↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

An Artificial-Intelligence and Machine-Learning-Based Methodology to Conduct Seemingly Strain-Controlled Fatigue Test in a Pressurized-Water-Reactor-Test-Loop-Autoclave, While Not Controlling the Strain

In general, low cycle fatigue analysis of pressurized water reactor (PWR) components, requires strain-controlled fatigue test data such as using strain versus life (ε–N) curves. Conducting strain-controlled fatigue tests under in-air conditions is not an issue. However, controlling strain in a PWR-test-loop-autoclave is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-test-loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulties of using an extensometer inside an autoclave led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting in substantially different cyclic strain amplitudes and their rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an Artificial-Intelligence and Machine-Learning based technique such as using k-means clustering technique to improve the pull-rod-control based fatigue test method, such that the gage-area strain amplitude and rates can reasonably be achieved. In support of this, we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.

42 ENGINEERING↗
DOE OSTI2021

Comparison of Rotary and Linear Cutting Methodology in Determining Specific Cutting Energy of Granite

Abstract Single polycrystalline diamond compact (PDC) cutting is a practical technique to understand the rock–tool interactions in drag-bit-type geothermal drilling operations. This paper introduces a rotary cutting method to determine specific cutting energy (SCE) and compares it with the conventional linear cutting method. In this work, granite is selected to represent hard rock formations in geothermal drilling. Cutting tests are conducted on a Computer Numerical Control (CNC) machine with a realistic cutting speed of 12.7 m/min and several chip loads ranging from 0.08 to 0.25 mm. The cutting force is measured using a dynamometer and then converted to specific cutting energy (SCE). The results show that the rotary method produces an inverse relationship between SCE and chip load, whereas the linear method shows the opposite. As a result, the produced SCE by the rotary method tends to be lower than that of the linear method at a higher chip load at and over 0.16 mm. The difference may be attributed to the cutting configuration and associated force components.

Engineering↗
DOE OSTI2013

UNDERSTANDING FLOW OF ENERGY IN BUILDINGS USING MODAL ANALYSIS METHODOLOGY

It is widely understood that energy storage is the key to integrating variable generators into the grid. It has been proposed that the thermal mass of buildings could be used as a distributed energy storage solution and several researchers are making headway in this problem. However, the inability to easily determine the magnitude of the building’s effective thermal mass, and how the heating ventilation and air conditioning (HVAC) system exchanges thermal energy with it, is a significant challenge to designing systems which utilize this storage mechanism. In this paper we adapt modal analysis methods used in mechanical structures to identify the primary modes of energy transfer among thermal masses in a building. The paper describes the technique using data from an idealized building model. The approach is successfully applied to actual temperature data from a commercial building in downtown Boise, Idaho.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗
DOE OSTI2016

Investigations on RELAP5-3D to RELAP5-3D coupling methodology by PVMEXEC

In the framework of a BEPU (Best Estimate Plus Uncertainty) approach within the licensing process of a nuclear power plant, the need to extend the resources of nuclear system thermal-hydraulics codes, such as RELAP5-3D, arises to allow more detailed simulations of the complex 3D reality of Nuclear Power Plants (NPPs), either under normal steady-state or during various accident scenarios. Currently, it is not possible to achieve the same degree of detail for a whole nuclear system when it is simulated with RELAP5-3D and this is due to the inherent limitations in the number of components and volumes to be used for the analysis. For this reason, it is of extreme interest the use of tools for codes coupling that enable the use of different codes for the simulation of different portions of a system in a unified analysis. In this paper the attention will be focused on the decomposition of the thermal-hydraulic domain of a system into subsystems to be simulated by different instances of the same code (e.g. RELAP5-3D) coupled together by means of PVMEXEC program and parallel virtual machine (PVM) technology. Explicit and semi-implicit solution algorithms were used for the analyses. Among the analyzed cases, the following will be discussed in detail with the aim to provide additional guidelines for the use of the PVMEXEC tool: (i) the Edward's pipe blowdown test, (ii) a simplified countercurrent heat exchanger, (iii) different hydraulics and heat structure coupling schemes for a shell-tube heat exchanger and (iv) a three-task coupled model of an adaptation of the Christensen subcooled boiling experiment. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗