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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

HERCULES: Strong Patterns towards More Intelligent Predictive Modeling, In: 2014 43rd International Conference on Parallel Processing

Recent work has shown that program analysis techniques to select meaningful code features of programs are important in the task of deciding the best compiler optimizations. Although, there are many successful state-of-the-art program analysis techniques, they often do not provide a simple method to extract the most expressive information about loops, especially when a target program is computationally intensive with complex loops and data dependencies. In this paper, we introduce a static technique to characterize a program using a pattern-driven system named HERCULES. This characterization technique not only helps a user to understand programs by searching pattern-of-interests, but also can be used for a predictive model that effectively selects the proper compiler optimizations. We formulated 35 loop patterns, then evaluated our characterization technique by comparing the predictive models constructed using HERCULES to three other state-of-the-art characterization methods. We show that our models outperform three state-of-the-art program characterization techniques on two multicore systems in selecting the best optimization combination from a given loop transformation space. We achieved up to 67% of the best possible speedup achievable with the optimization search space we evaluated.

Computer Science↗
DOE OSTI2017

Adaptive building load control to enable high penetration of solar photovoltaic generation

This paper investigates the ability of a collection of deferrable heating, ventilation and air conditioning (HVAC) loads to directly emulate energy storage devices (ESD), that is, to absorb any low frequency fluctuations in solar generation. We find the optimal number of aggregated HVAC loads to absorb solar fluctuation in the same frequency band based on frequency analysis. To guarantee quality of service in afleet of residential buildings, an optimization problem is formed to compute optimal schedule of the HVAC loads considering temperature comfort band and available solar generation. By minimizing the tracking error between solar generation and total consumption, the proposed method also quantifies the minimum capacity of ESD (battery or fly-wheel) . The simulation results show that the proposed techniques are able to achieve good solar tracking performance as well as obtain a minimal ESD capacity. Therefore, we show that most of the renewable generation can be consumed locally through an intelligent coordination of HVAC loads, their impacts on the grid would be minimal and the required ESD capacity can be minimized.

Dong, Jin↗
DOE OSTI2018

Cross-geography scientific data transferring trends and behavior

Wide area data transfers play an important role in many science applications but rely on expensive infrastructure that often delivers disappointing performance in practice. In response, we present a systematic examination of a large set of data transfer log data to characterize transfer characteristics, including the nature of the datasets transferred, achieved throughput, user behavior, and resource usage. This analysis yields new insights that can help design better data transfer tools, optimize networking and edge resources used for transfers, and improve the performance and experience for end users. Our analysis shows that (i) most of the datasets as well as individual files transferred are very small; (ii) data corruption is not negligible for large data transfers; and (iii) the data transfer nodes utilization is low. Insights gained from our analysis suggest directions for further analysis.

Liu, Zhengchun↗
DOE OSTI2017

Evaluating Tilt for Wind Plants

The objective of this work is to demonstrate the feasibility of tilt in a wind plant. Tilt control, much like other wind plant control strategies, has the potential to improve the performance of a wind plant. Tilt control uses the tilt angle of the turbine to direct the wake above or below the downstream turbines. This paper presents a study of tilt in two- and three-turbine arrays. Specifically, the authors show that the power production of a two-turbine array can be increased by tilting turbines in a specific orientation. When adding more turbines, as is shown with the three-turbine array, the overall percentage of power gain increases. This outcome deviates from some of the results seen in typical wind plant control strategies. Finally, we discuss the impact this type of control strategy has on the aerodynamics in a wind plant. This analysis demonstrates that a good understanding of wake characteristics is necessary to improve the plant's performance. A tilt strategy such as the one presented in this paper may have implications for future control/optimization studies including optimization of hub heights in a wind plant and analysis of deep array effects.

17 WIND ENERGY↗
DOE OSTI2017

Evaluating Tilt for Wind Farms: Preprint

The objective of this work is to demonstrate the feasibility of tilt in a wind plant. Tilt control, much like other wind plant control strategies, has the potential to improve the performance of a wind plant. Tilt control uses the tilt angle of the turbine to direct the wake above or below the downstream turbines. This paper presents a study of tilt in two- and threeturbine arrays. Specifically, the authors show that the power production of a two-turbine array can be increased by tilting turbines in a specific orientation. When adding more turbines, as is shown with the three-turbine array, the overall percentage of power gain increases. This outcome deviates from some of the results seen in typical wind plant control strategies. Finally, we discuss the impact this type of control strategy has on the aerodynamics in a wind plant. This analysis demonstrates that a good understanding of wake characteristics is necessary to improve the plant's performance. A tilt strategy such as the one presented in this paper may have implications for future control/optimization studies including optimization of hub heights in a wind plant and analysis of deep array effects.

17 WIND ENERGY↗
DOE OSTI2018

Secondary carbides in high chromium cast irons: An alternative approach to their morphological and spatial distribution characterization

Highlights: • Systematic characterization of second phase particles by combining a variety of techniques • Comparison of morphological parameters obtained from SEM, BSE, etching+CLSM and EBSD • Quantitative analysis and degree of distribution of secondary carbides • Particle size, distribution and morphology from image analysis of CLSM, SEM and EBS images - Abstract: Secondary carbides precipitated in high chromium cast irons during thermal treatments were characterized by means of different characterization techniques, including scanning electron microscopy, energy dispersive X-ray spectroscopy, electron backscattered diffraction and a combination of chemical etching with confocal scanning laser microscopy. This set of techniques provides a full morphological, chemical and crystallographic description of the analysed phases. This work evaluated different methods for optimizing the image acquisition for a further image analysis (IA) based on the threshold binarization. Finally, the carbide size, distribution and morphology were determined after IA of the images acquired by aforementioned characterization techniques. Although the different techniques report some dispersion in the value for the average particle size, the particle inter-spacing and aspect ratio meet within the error value. The proposed characterization methodology provides statistically reliable data for a further evaluation of related physical properties in composites.

36 MATERIALS SCIENCE↗
DOE OSTI2020

GPU-Based Static Data-Flow Analysis for Fast and Scalable Android App Vetting

Many popular vetting tools for Android applications use static code analysis techniques. In particular, Interprocedural Data-Flow Graph (IDFG) construction is the computation at the core of Android static data-flow analysis and consumes most of the analysis time. Many analysis tools use a worklist algorithm, an iterative fixed-point approach, to construct the IDFG. In this paper, we observe that a straightforward GPU parallelization of the worklist algorithm leads to significant underutilization of the GPU resources. We identify four performance bottlenecks, namely, frequent dynamic memory allocations, high branch divergence, workload imbalance, and irregular memory access patterns. Accordingly, we propose GDroid, a GPU-based worklist algorithm implementation with multiple fine-grained optimizations tailored to common characteristics of Android applications. The optimizations considered are: matrix-based data structure, memory access-based node grouping, and worklist merging. Our experimental evaluation, performed on 1000 Android applications, shows that the proposed optimizations are beneficial to performance, and GDroid can achieve up to 128X speedups against a plain GPU implementation.

Android security↗
DOE OSTI2024

Practical Insights on Applying Simulation-Based Control Methods in Experimental Studies

Advanced nuclear reactors are crucial to the future of energy both in the United States and around the globe. In contrast to the current operating fleet, they are characterized as being deployable in remote locations and able to operate in semi-autonomous or autonomous fashion. This leap forward necessitates a new reactor control paradigm. Because advanced nuclear reactors are still under development in the United States, the creation of new control methods to achieve autonomous operations has been based on systems modeling and simulation. However, an important factor in successfully deploying these new control methods is the ability to seamlessly transition from simulation environments to real-world settings. Control methods tested in both simulation and experimental settings need to be investigated in the context of advanced reactor applications. This work developed a series of simple controllers for Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor operating in load-following scenarios. These controllers were tested in both simulation and experimental settings, and a comparative performance analysis was performed. The simulation tests leveraged the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software developed in a previous stage of the current effort, along with the MARVEL Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The experimental tests leveraged the COMMAND software, MARVEL models, and the U.S. Department of Energy Microreactor Program’s Microreactor Automated Control System (MACS). MACS was developed to serve as a control method testbed. It was customized to mirror the MARVEL microreactor, and COMMAND enabled MACS to emulate the physics of MARVEL. The load-following controller was developed using the simulation platform, with efforts to emulate real systems by introducing actuator saturation and noise. These factors were incrementally accounted for in the controller design. After finalizing the controller design, it was implemented with the experimental setup. The experimental conditions tested included an initial test under conditions similar to the final simulation test, and two additional scenarios. The first scenario introduced additional actuator saturation to account for equipment aging over time, which was unknown to the controller. The second scenario introduced sensor delay, a phenomenon anticipated with the use of remote operations or wireless communication in advanced reactors. These tests revealed several notable differences. While the controller performed well in simulation, it exhibited several limitations when transitioning to hardware. The main challenges involved maintaining the steady-state target power, as evidenced by larger error values between the true reactor power and setpoint power, as well as persistent oscillations in controlled reactor power. These issues could lead to unacceptable transient conditions in real reactor testing. Introducing actuator aging and stochastic delays in the experimental setup significantly impacted controller performance, resulting in increased overshoot and undershoot, and exacerbated error and oscillations previously mentioned. These findings underscore the importance of experimental testbeds for testing and validating control methods, as controllers developed using only theory and/or simulation may perform unexpectedly when applied to actual hardware. This research emphasizes the need for an experimental testbed for achieving such validation.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Symptomatic Pericardial Effusion After Chemoradiation Therapy in Esophageal Cancer Patients

Purpose: We investigated clinical and treatment-related factors as predictors of symptomatic pericardial effusion in esophageal cancer patients after concurrent chemoradiation therapy. Methods and Materials: We reviewed 214 consecutive primary esophageal cancer patients treated with concurrent chemoradiation therapy between 2001 and 2010 in our institute. Pericardial effusion was detected on follow-up computed tomography. Symptomatic effusion was defined as effusion ≥grade 3 according to Common Terminology Criteria for Adverse Events v4.0 criteria. Percent volume irradiated with 5 to 65 Gy (V5-V65) and mean dose to the pericardium were evaluated employing dose-volume histograms. To evaluate dosimetry for patients treated with two-dimensional planning in the earlier period (2001-2005), computed tomography data at diagnosis were transferred to a treatment planning system to reconstruct three-dimensional plans without modification. Optimal dosimetric thresholds for symptomatic pericardial effusion were calculated by receiver operating characteristic curves. Associating clinical and treatment-related risk factors for symptomatic pericardial effusion were detected by univariate and multivariate analyses. Results: The median follow-up was 29 (range, 6-121) months for eligible 167 patients. Symptomatic pericardial effusion was observed in 14 (8.4%) patients. Dosimetric analyses revealed average values of V30 to V45 for the pericardium and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those with asymptomatic pericardial effusion (P<.05). Pericardial V5 to V55 and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those without pericardial effusion (P<.001). Mean pericardial doses of 36.5 Gy and V45 of 58% were selected as optimal cutoff values for predicting symptomatic pericardial effusion. Multivariate analysis identified mean pericardial dose as the strongest risk factor for symptomatic pericardial effusion. Conclusions: Dose-volume thresholds for the pericardium facilitate predicting symptomatic pericardial effusion. Mean pericardial dose was selected based not only on the optimal dose-volume threshold but also on the most significant risk factor for symptomatic pericardial effusion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

An Evaluation of the Economic and Resilience Benefits of a Microgrid in Northampton, Massachusetts

Recent developments and advances in distributed energy resource (DER) technologies make them valuable assets in microgrids. This paper presents an innovative evaluation framework for microgrid assets to capture economic benefits from various grid and behind-the-meter services in grid-connecting mode and resilience benefits in islanding mode. In particular, a linear programming formulation is used to model different services and DER operational constraints to determine the optimal DER dispatch to maximize economic benefits. For the resiliency analysis, a stochastic evaluation procedure is proposed to explicitly quantify the microgrid survivability against a random outage, considering uncertainties associated with photovoltaic (PV) generation, system load, and distributed generator failures. Optimal coordination strategies are developed to minimize unserved energy and improve system survivability, considering different levels of system connectedness. The proposed framework has been applied to evaluate a proposed microgrid in Northampton, Massachusetts that would link the Northampton Department of Public Works, Cooley Dickenson Hospital, and Smith Vocational Area High School. The findings of this analysis indicate that over a 20-year economic life, a 441 kW/441 kWh battery energy storage system, and 386 kW PV solar array can generate $2.5 million in present value benefits, yielding a 1.16 return on investment ratio. Results of this study also show that forming a microgrid generally improves system survivability, but the resilience performance of individual facilities varies depending on power-sharing strategies.

25 ENERGY STORAGE↗
DOE OSTI2019

Summary Report of the Tri-Lab Workshop on Modeling & Analysis of Current & Future Energy Systems

The workshop identified how modeling and analysis can be used for energy system design, optimization, and planning to help identify opportunities to enhance the performance and potential of current and future energy systems, with a specific focus on integrated, hybrid energy systems. Comprehensive understanding of these systems requires models at different scales (from market to grid to process system to energy device). As discussed within the context of three detailed case studies, there is a need to develop useful datasets for a variety of purposes and to extend and connect existing models and analysis tools across scales and across domains to address the challenges associated with developing technologies to enable the integrated energy systems of the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Fully Automated Laser Ablation Liquid Capture Sample Analysis using NanoElectrospray Ionization Mass Spectrometry

RATIONALE: Laser ablation provides for the possibility of sampling a large variety of surfaces with high spatial resolution. This type of sampling when employed in conjunction with liquid capture followed by nanoelectrospray ionization provides the opportunity for sensitive and prolonged interrogation of samples by mass spectrometry as well as the ability to analyze surfaces not amenable to direct liquid extraction. METHODS: A fully automated, reflection geometry, laser ablation liquid capture spot sampling system was achieved by incorporating appropriate laser fiber optics and a focusing lens into a commercially available, liquid extraction surface analysis (LESA ) ready Advion TriVersa NanoMate system. RESULTS: Under optimized conditions about 10% of laser ablated material could be captured in a droplet positioned vertically over the ablation region using the NanoMate robot controlled pipette. The sampling spot size area with this laser ablation liquid capture surface analysis (LA/LCSA) mode of operation (typically about 120 m x 160 m) was approximately 50 times smaller than that achievable by direct liquid extraction using LESA (ca. 1 mm diameter liquid extraction spot). The set-up was successfully applied for the analysis of ink on glass and paper as well as the endogenous components in Alstroemeria Yellow King flower petals. In a second mode of operation with a comparable sampling spot size, termed laser ablation/LESA , the laser system was used to drill through, penetrate, or otherwise expose material beneath a solvent resistant surface. Once drilled, LESA was effective in sampling soluble material exposed at that location on the surface. CONCLUSIONS: Incorporating the capability for different laser ablation liquid capture spot sampling modes of operation into a LESA ready Advion TriVersa NanoMate enhanced the spot sampling spatial resolution of this device and broadened the surface types amenable to analysis to include absorbent and solvent resistant materials.

alstroemeria↗
DOE OSTI2019

Calibration of Car-Following Models Using a Dual Genetic Algorithm with Central Composite Design

We report the calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. Finally, this method can be extended to other optimization tasks using GA in transportation studies.

42 ENGINEERING↗
DOE OSTI2024

Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience

Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Methods identifying cost reduction potential for water electrolysis systems

The electrochemical reduction of water to form hydrogen is an emerging alternative for energy storage, where hydrogen can be used as a transportation fuel, combusted to generate electricity, utilized in a fuel cell, or used as a feedstock for chemical synthesis. Techno-economic analysis (TEA) is a valuable tool for understanding how to inform research directions that could make hydrogen from electrolysis cost competitive with that produced by conventional means like steam methane reforming. The current state of knowledge on TEA of electrolysis systems suggests cost reductions are likely to result from advances in system design and materials, scale-up of manufacturing processes, and learning by doing effects as electrolysis deployment increases. Future directions and opportunities for TEA of electrolysis systems include dispatchable operation in wholesale power markets, optimization of capital cost and system durability, and analysis of pathways for hydrogen to support economy-wide decarbonization.

08 HYDROGEN↗