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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

OTERR User Manual

OTERR (Optimization of TEst Reactor Reloading) is a software tool created to assist in the determination of optimal fuel reloading patterns for test reactors. The intended application is for the Versatile Test Reactor (VTR) program, but it provides functions that could be useful for analysis and optimization of many types of fast reactors. OTERR does not perform neutron/gamma transport, heat transfer, thermal hydraulics, or depletion calculations. Instead, it acts as a wrapper around codes that provide these capabilities, with a native genetic algorithm optimization capability. At this time, wrapping is only implemented for Argonne Reactor Computation (ARC) codes DIF3D, REBUS, and GAMSOR, and SE2-ANL. OTERR also has capabilities to facilitate input creation for DASSH, a thermal hydraulics code similar to SE2- ANL being developed for the VTR program.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

The effect of reducing dietary lipid and food availability on precocious male maturation in Chinook Salmon: A production-scale hatchery experiment

Abstract Objective Age of maturation in Chinook Salmon Oncorhynchus tshawytscha is phenotypically plastic, influenced by both genotype and environmental factors, including the availability and composition of the diet. Salmon hatchery programs often rear fish under accelerated growth regimes using high-lipid diets that can result in earlier age at maturity, including increased prevalence of age-2 males (minijacks). The goal of this investigation was to compare alternative dietary regimes to mitigate for this shift in age at maturity in hatchery-reared Umatilla River fall Chinook Salmon. Methods Juvenile fish were reared at Bonneville Hatchery, Oregon, under four dietary treatments across four replicate brood years. Dietary treatments included two feeding frequencies (standard [fed 7 days/week] and reduced [fed 4 days/week]) and two dietary lipid levels (standard [18%] and reduced [12%]) in a 2 × 2 factorial design. Dietary treatments were applied for approximately 9 months, beginning in March (a month after fry emergence) and lasting until December of the first year, after which all fish were reared on the standard feeding regime (7 days–18%) until the time of release the following spring as yearlings. Result We observed significant interannual variation in the proportion of minijacks produced among dietary treatments. For all brood years, decreasing the feeding frequency from 7 to 4 days/week reduced the proportion minijacks by 35.9%, and lowering dietary lipid from 18% to 12% reduced the proportion minijacks by 30%. The combined effects of reducing the feeding frequency and lowering dietary lipid were additive, reducing the proportion minijacks by 65.5% compared to the standard rearing regime. Growth and energetic indices were monitored throughout and confirmed findings from previous laboratory-based studies indicating that physiological status 10–12 months prior to spawn timing is important for the “decision” to mature. Conclusion Results of this investigation provide useful insights for optimizing rearing regimes for the Umatilla River program and other Chinook Salmon hatchery programs.

Harstad, Deborah L. (ORCID:0000000202676085)↗
DOE OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗
DOE OSTI2012

Fuel Flexible Turbine System (FFTS) Program

In this fuel flexible turbine system (FFTS) program, the Parker gasification system was further optimized, fuel composition of biomass gasification process was characterized and the feasibility of running Capstone MicroTurbine(TM) systems with gasification syngas fuels was evaluated. With high hydrogen content, the gaseous fuel from a gasification process of various feed stocks such as switchgrass and corn stover has high reactivity and high flashback propensity when running in the current lean premixed injectors. The research concluded that the existing C65 microturbine combustion system, which is designed for natural gas, is not able to burn the high hydrogen content syngas due to insufficient resistance to flashback (undesired flame propagation to upstream within the fuel injector). A comprehensive literature review was conducted on high-hydrogen fuel combustion and its main issues. For Capstone's lean premixed injector, the main mechanisms of flashback were identified to be boundary layer flashback and bulk flow flashback. Since the existing microturbine combustion system is not able to operate on high-hydrogen syngas fuels, new hardware needed to be developed. The new hardware developed and tested included (1) a series of injectors with a reduced propensity for boundary layer flashback and (2) two new combustion liner designs (Combustion Liner Design A and B) that lead to desired primary zone air flow split to meet the overall bulk velocity requirement to mitigate the risk of core flashback inside the injectors. The new injector designs were evaluated in both test apparatus and C65/C200 engines. While some of the new injector designs did not provide satisfactory performance in burning target syngas fuels, particularly in improving resistance to flashback. The combustion system configuration of FFTS-4 injector and Combustion Liner Design A was found promising to enable the C65 microturbine system to run on high hydrogen biomass syngas. The FFTS-4 injector was tested in a C65 engine operating on 100% hydrogen and with the redesigned combustion liner - Combustion Liner Design A - installed. The results were promising for the FFTS program as the system was able to burn 100% hydrogen fuel without flashback while maintaining good combustion performance. While initial results have been demonstrated the feasibility of this program, further research is needed to determine whether these results will be repeated with FFTS-4 injectors installed in all injector ports and over a wide range of operating conditions and fuel variations.

42 ENGINEERING↗
DOE OSTI2022

Work Smarter, Not Harder: Improving Energy Efficiency and Safety through Smarter Ventilation: Preprint

Ventilation is a key component to maintaining healthy, safe indoor air quality. Especially important in laboratories, ventilation is the first line of defense against airborne hazards produced during research activities. Though a vital component, laboratory ventilation systems are often victim to ineffective operation, posing a risk to the most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. To improve both energy efficiency and safety in laboratories, we present the Smart Labs Toolkit - a resource developed by the U.S. Department of Energy Federal Energy Management Program and the International Institute for Sustainable Laboratories that guides laboratory stakeholders through a straight-forward, holistic approach to achieve dynamic, high-performance laboratories. Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. A Smart Labs program employs a combination of physical, administrative, and management techniques to assess, optimize, and manage high performance laboratories. We will focus on a central component of the Smart Labs approach - the Laboratory Ventilation Risk Assessment, a systematic process for identifying risk due to airborne hazards to inform the operation of dynamic ventilation that optimizes safety and efficiency. Case studies of organizations who have successfully implemented Smart Labs ventilation management programs will also be shared. In learning ventilation strategies successful in critical laboratory environments, learn the tools and resources needed to successfully manage energy in any building through smarter, safer ventilation.

buildings↗
DOE OSTI2011

Steady State Heat Deposits Modeling in the Nb3Sn Quadrupole Magnets for the Upgrade of the LHC Inner Triplet

In hadron colliders such as the LHC, the energy deposited in the superconductors by the particles lost from the beams or coming from the collision debris may provoke quenches detrimental to the accelerator operation. In previous papers, a Network Model has been used to study the thermodynamic behavior of magnet coils and to calculate the quench levels in the LHC magnets for expected beam loss profiles. This model was subsequently used for thermal analysis and design optimization of Nb{sub 3}Sn quadrupole magnets, which LARP (US LHC Accelerator Research Program) is developing for possible use in the LHC luminosity upgrade. For these new magnets, the heat transport efficiency from the coil to the helium bath needs to be determined and optimized. In this paper the study of helium cooling channels and the heat evacuation scheme are presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Exploiting Identical Generators in Unit Commitment

Here, we present sufficient conditions under which thermal generators can be aggregated in mixed-integer linear programming (MILP) formulations of the unit commitment (UC) problem, while maintaining feasibility and optimality for the original disaggregated problem. Aggregating thermal generators with identical characteristics (e.g., minimum/maximum power output, minimum up/down-time, and cost curves) into a single unit reduces redundancy in the search space induced by both exact symmetry (permutations of generator schedules) and certain classes of mutually non-dominated solutions. We study the impact of aggregation on two large-scale UC instances, one from the academic literature and another based on real-world operator data. Our computational tests demonstrate that when present, identical generators can negatively affect the performance of modern MILP solvers on UC formulations. Further, we show that our reformation of the UC MILP through aggregation is an effective method for mitigating this source of computational difficulty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Universal Optimization and Tighter Fidelity Bounds for Approximate Quantum Error Correction

Approximate quantum error correction (AQEC) not only dictates the performance of discrete- and continuous-variable quantum error correction codes but also serves as a unifying framework across various physical disciplines. Identifying the optimal recovery channel to maximize the entanglement fidelity via standard semidefinite programming is computationally bottlenecked by the exponentially growing number of Kraus operators with system size, rendering large-scale optimization prohibitive. While analytical near-optimal maps exist, they typically work only when the Knill-Laflamme conditions are nearly satisfied. In this Letter, we establish an efficient framework by leveraging the duality between recovery and environment decoupling. This framework yields a tighter analytical lower bound on entanglement fidelity than the conventional limit set by the transpose channel. Furthermore, by exploiting the decayed weights of noise Kraus operators, we introduce a framework based on principal component analysis to reduce the dimension. In thermal loss channels where the weights decay exponentially, our approach achieves a 33-fold computational speedup while maintaining rigorous accuracy. Our approach enables high-precision optimization for AQEC codes that were previously intractable due to the curse of dimensionality.

Wu, Jing [Fermilab] (ORCID:0000000249460732)↗
DOE OSTI2018

Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

Distribution system operators (DSOs) world-wide foresee a rapid roll-out of distributed energy resources. From the system perspective, their reliable and cost effective integration requires accounting for their physical properties in operating tools used by the DSO. This work describes an decomposable approach to leverage the dispatch flexibility of thermostatically controlled loads (TCLs) for operating distribution systems with a high penetration level of photovoltaic resources. Each TCL ensemble is modeled using the Markov Decision Process (MDP). The MDP model is then integrated with a chance constrained optimal power flow that accounts for the uncertainty of PV resources. Since the integrated optimization model cannot be solved efficiently by existing dynamic programming methods or off-the-shelf solvers, this paper proposes an iterative Spatio-Temporal Dual Decomposition algorithm (ST-D2). Finally, we demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Optimizing Geothermal Drilling: Oil and Gas Technology Transfer

There is a significant amount of financial risk associated with geothermal drilling. This study of drilling operations seeks opportunities to improve upon current practices and technologies. The scope of this study included analyzing 21 geothermal wells and 21 oil and gas wells. The goal was to determine a 'Perfect Well' using historical data to compare the best oil and gas well to the best geothermal well. Unfortunately, limitations encountered in the study included missing data (bit records, mud information, etc.) and poor data collection practices An online software database was used to format drilling data to IADC coded daily drilling reports and generate figures for analysis. Six major issues have been found in geothermal drilling operations. These problems include lost circulation, rig/ equipment selection, cementing, penetration rate, drilling program, and time management. As a result of these issues, geothermal drilling averaged 56.4 days longer than drilling comparable oil and gas wells in the wells in this study. Roughly $13.9 million was spent on non-productive time in the 21 geothermal wells, compared with only $1.3 million in the oil and gas wells, assuming a cost of $50,000 per day. Comparable events such as drilling the same sized hole, tripping in/out, cementing, and running the same size casing took substantially less time in the oil and gas wells. Geothermal wells were drilled using older and/or less advanced technology to depths less than 10,000 feet, while oil and gas wells reached 12,500 feet faster with purpose built rigs. A new approach is now underway that will optimize drilling programs throughout the drilling industry using Mechanical Specific Energy (MSE) as a tool to realize efficient drilling processes. Potential improvements for current geothermal operations are: the use of electronic records, real time services, and official glossary terms to describe rig operations, and advanced drilling rigs/technology.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Optimizing Geothermal Drilling: Oil and Gas Technology Transfer

There is a significant amount of financial risk associated with geothermal drilling; however, there are opportunities to improve upon current practices and technologies used. The scope of this drilling operational study included 21 geothermal wells and 21 oil and gas wells. The goal was to determine a 'perfect well' using historical data to compare the best oil and gas well to the best geothermal well. Unfortunately, limitations encountered in the study included missing data (bit records, mud information, etc.), poor data collection, and difficult to ascertain handwriting. An online software database was used to format drilling data to IADC coded daily drilling reports and generate analysis figures. Six major issues have been found in geothermal drilling operations. These problems include lost circulation, rig/equipment selection, cementing, penetration rate, drilling program, and time management. As a result of these issues, geothermal drilling averages 56.4 days longer than drilling comparable oil and gas wells in the wells in this study. Roughly $13.9 million would be lost due to non-productive time in the 21 geothermal wells and only $1.3 million in the oil and gas wells, assuming a cost of $50,000 per day. Comparable events such as drilling the same sized hole, tripping in/out, cementing, and running the same size casing took substantially less time in the oil and gas wells. Geothermal wells were drilled using older and/or less advanced technology to depths less than 10,000 feet, while oil and gas wells reached 12,500 feet faster with purpose built rigs. A new approach is now underway that will optimize drilling programs throughout the drilling industry. It is the use of Mechanical Specific Energy (MSE) as a tool to realize efficient drilling processes. However, a work-flow must also be established in order for there to be an efficient drilling program. Potential improvements for current geothermal operations are: the use of electronic records, real time services, and official glossary terms to describe rig operations, and advanced drilling rigs/technology.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Evaluating the networking characteristics of the Cray XC-40 Intel Knights Landing-based Cori supercomputer at NERSC

There are many potential issues associated with deploying the Intel Xeon Phi™ (code named Knights Landing [KNL]) manycore processor in a large-scale supercomputer. One in particular is the ability to fully utilize the high-speed communications network, given that the serial performance of a Xeon Phi TM core is a fraction of a Xeon®core. In this paper, we take a look at the trade-offs associated with allocating enough cores to fully utilize the Aries high-speed network versus cores dedicated to computation, e.g., the trade-off between MPI and OpenMP. In addition, we evaluate new features of Cray MPI in support of KNL, such as internode optimizations. We also evaluate one-sided programming models such as Unified Parallel C. We quantify the impact of the above trade-offs and features using a suite of National Energy Research Scientific Computing Center applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Optimizing I/O Forwarding Techniques for Extreme-Scale Event Tracing

Programming development tools are a vital com- ponent for understanding the behavior of parallel applica- tions. Event tracing is a principal ingredient to these tools, but new and serious challenges place event tracing at risk on extreme-scale machines. As the quantity of captured events increases with concurrency, the additional data can over- load the parallel file system and perturb the application be- ing observed. In this work we present a solution for event tracing on extreme-scale machines. We enhance an I/O for- warding software layer to aggregate and reorganize log data prior to writing to the storage system, significantly reduc- ing the burden on the underlying file system. Furthermore, we introduce a sophisticated write buffering capability to limit the impact. To validate the approach, we employ the Vampir tracing toolset using these new capabilities. Our re- sults demonstrate that the approach increases the maximum traced application size by a factor of 5x to more than 200,000 processes.

atomic append↗
DOE OSTI2023

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. As a result, the adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Layer-by-layer charging in non-volatile memory devices using embedded sub-2 nm platinum nanoparticles

In this work, we demonstrate multi-level operation of a non-volatile memory metal oxide semiconductor capacitor by controlled layer-by-layer charging of platinum nanoparticle (PtNP) floating gate devices with defined gate voltage bias ranges. The device consists of two layers of ultra-fine, sub-2 nm PtNPs integrated between Al{sub 2}O{sub 3} tunneling and separation layers. PtNP size and interparticle distance were varied to control the particle self-capacitance and associated Coulomb charging energy. Likewise, the tunneling layer thicknesses were also varied to control electron tunneling to the first and second PtNP layers. The final device configuration with optimal charging behavior and multi-level programming was attained with a 3 nm Al{sub 2}O{sub 3} initial tunneling layer, initial PtNP layer with particle size 0.54 ± 0.12 nm and interparticle distance 4.65 ± 2.09 nm, 3 nm Al{sub 2}O{sub 3} layer to separate the PtNP layers, and second particle layer with 1.11 ± 0.28 nm PtNP size and interparticle distance 2.75 ± 1.05 nm. In this device, the memory window of the first PtNP layer saturated over a programming bias range of 7 V to 14 V, after which the second PtNP layer starts charging, exhibiting a multi-step memory window with layer-by-layer charging.

36 MATERIALS SCIENCE↗