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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAs that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo . Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.

Weidmann, Chase A.↗
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Used Fuel Dry Storage Systems and Components for Shutdown Plants to Expedite Defueling of Spent Fuel Pool - 18378

For plants that have announced plans to shut down, their main priority is to off-load the Used Nuclear Fuel from the spent fuel pool to dry storage at an ISFSI so that they can proceed to 'Safe store' or other storage options, and start their decommissioning process as soon as possible. One of the major expenses for a decommissioned plant is the cost of maintenance of the spent fuel pool. Therefore, the ability of the dry storage system to store short cooled fuel is an important aspect of dry storage system for shutdown plants. When a shutdown plant evaluates the dry storage options, total cost of the transferring the fuel from the pool to ISFSI site needs to be evaluated. One of the major costs to the utility for dry storage is the cost of ISFSI pad (basemat) along with the cost of dry storage system hardware. Smaller ISFSI footprint saves utility for the cost of ISFSI site excavation, cost of engineered backfill and cost of ISFSI pad. In order to minimize the cost of dry storage systems, besides the size of the ISFSI pad as described above, other important considerations is to use high capacity systems. The highest capacity systems available in the dry storage market today are with 37 PWR or 89 BWR used fuel assemblies per canister. These dry storage systems are expected to be at the plant sites for extended duration due to the unavailability of Interim Storage Facilities or a permanent disposal sites like Yucca Mountain. Maintaining the integrity of the dry storage systems canisters and storage overpack so that they can safely protect the fuel and integrity of used nuclear fuel is maintained during storage period is of utmost importance. Robust Aging Management Program is an important aspect for dry storage systems not only at the shutdown plants but also at the operating plants. This paper will provide the update of a reduced footprint version of TN Americas NUHOMS{sup R} System with MATRIX HSM and NUHOMS{sup R} EOS Canisters that addresses the need for maximizing safety, ISFSI capacity while optimizing cost. It will provide an update on the current capabilities, design features and current status of the licensing and deployment of NUHOMS{sup R} EOS System. It will provide the current status of the MATRIX HSM along with the significant enhancement included for a robust Canister Evolved Safety Program for Aging Management. It will also describe the construction sequence; loading operations, and inspections during extended storage as a part of aging management program. The compact version of TN Americas NUHOMS{sup R} System with MATRIX HSM offers a 'Universal' Storage Overpack which is designed to accept all different designs dry storage canisters available in the industry. As UNF storage continues to drives the industry towards innovations that address the need for optimized dry storage systems, compact ISFSIs and prudent aging management programs, this new evolution of the TN Americas NUHOMS{sup R} system with MATRIX HSM and EOS Canisters offers a significant leap forward in addressing all of these challenges. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

The Value of Hyperparameter Optimization in Phase-Picking Neural Networks

The effectiveness of neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters, limiting their performance. In this study, we use phase picks from both routine and template-matching analyses from multiple regions (Ridgecrest, California; Kilauea, Hawaii; Yellowstone, Wyoming–Montana–Idaho) to test a hyperparameter optimization scheme for phase-picking neural networks and to evaluate their performance. We show that a published model, namely PhaseNet (Zhu and Beroza, 2019), can be simplified and improved with reasonable effort and there are preferred choices of hyperparameters that increase the performance. We also show that models optimized based on the arrival times reported in routine event catalogs consistently perform well when picking arrival times of smaller events, which is crucial for many tasks from microseismicity to explosion monitoring.

58 GEOSCIENCES↗
DOE OSTI2016

Final Technical Report: Sparse Grid Scenario Generation and Interior Algorithms for Stochastic Optimization in a Parallel Computing Environment

The support from this grant resulted in seven published papers and a technical report. Two papers are published in SIAM J. on Optimization [87, 88]; two papers are published in IEEE Transactions on Power Systems [77, 78]; one paper is published in Smart Grid [79]; one paper is published in Computational Optimization and Applications [44] and one in INFORMS J. on Computing [67]). The works in [44, 67, 87, 88] were funded primarily by this DOE grant. The applied papers in [77, 78, 79] were also supported through a subcontract from the Argonne National Lab. We start by presenting our main research results on the scenario generation problem in Sections 1–2. We present our algorithmic results on interior point methods for convex optimization problems in Section 3. We describe a new ‘central’ cutting surface algorithm developed for solving large scale convex programming problems (as is the case with our proposed research) with semi-infinite number of constraints in Section 4. In Sections 5–6 we present our work on two application problems of interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

DP-Based Optimization of BESS to Substitute RICE Reserves for Improved Economic Benefits

In regions with a significant proportion of renewable energy sources, reciprocating internal combustion engines (RICE or IC) are frequently employed as grid reserve solutions. This study suggests a scheduling technique for battery energy storage systems (BESS) that utilizes dynamic programming (DP) to replace IC units, adding versatility to the total reserve capacity and resulting in higher BESS earnings compared to traditional scheduling approaches. The capacity sweeping approach to optimize BESS’s revenue sometimes leads to operation which is counter intuitive to conventional battery sizing approaches.

25 ENERGY STORAGE↗
DOE OSTI2017

Making Roads Safer: Optimizing De-icing Using Snowmelt Rates and Slope Data

More than $ 1.5 B is spent annually on snow-control programs in the United States. With an increase in severe winter storms, many municipalities are encountering difficulty in clearing their roads of ice and snow within their budgetary limits. Currently, roads are classified based on traffic counts, with busier (arterial) roads being treated before less busy (secondary) roads. This approach is problematic because most people live on secondary roads and must travel over these untreated roads before they can reach treated roads. In addition, inefficient distribution of de-icing material can have knock-on environmental impacts with increased material run-off into surrounding areas. To address these concerns, we developed a model for categorizing road vulnerability based on snowmelt rate and slope. We calculated the snowmelt rates in 1-m by 1-m squares of all roads in Knox County, Tennessee, using an empirical equation developed by the U.S. Army Corps of Engineers. This equation takes in freely available weather data and incident solar radiation, which we calculated based on LiDAR (Light Detection And Ranging) data. Using the model results, we propose a more efficient plan: apply de-icers to the steepest roads with the slowest snowmelt, rather than to the busiest roads. Under this approach, we argue that given existing budgetary constraints, more vulnerable roads can be treated with de-icing material and provide for easier mobility for motorists traveling on secondary roads. Our approach provides a more cost-effective, environmentally-conscious, and mobility enhancing application strategy.

Chapin, Fletcher↗
DOE OSTI2021

Extending C++ for Heterogeneous Quantum-Classical Computing

In this report we present qcor - a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Chefornak Energy Configuration Options: Energy Infrastructure Optimization to Reduce Fuel Cost and Dependence in Chefornak, Alaska

The U.S. Department of Energy is funding, through the Grid Modernization Program, the Alaska Microgrid Partnership (AMP), a multi-stakeholder collaborative comprised of national labs and Alaska based partners. The over-arching goal of AMP is to reduce the use of total imported fuel into communities to secure all energy services by at least 50% in Alaska's remote microgrids without increasing system lifecycle costs, while improving overall system reliability, security, and resilience. One goal of AMP is to investigate if a combination of Energy Efficiency (EE) and high contribution (from Renewable Energy (RE)) power systems can reduce total imported energy usage by 50% while lowering Life Cycle Costs (LCC) and improving reliability and resiliency. A techno-economic analysis was conducted for the community of Chefornak to investigate system architectures that could meet the goal of reducing imported fuel consumption by 50% or more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Optimization and Verification of FRAM (V.6.1) Parameter Sets

FRAM (Fixed-energy Response-function Analysis with Multiple efficiency) is a program developed at Los Alamos National Laboratory that determines actinide isotopic composition by analyzing gamma-ray spectra of special nuclear material. This report documents the creations of the parameter sets and the performance (bias and precision) of FRAM version 6.1, scheduled for release in late 2019 – early 2020.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Design Optimization for Printed Melt Wire Arrays Encapsulation

As part of the Nuclear Energy Enabling Technology (NEET) Advanced Sensor and Instrumentation (ASI) Program, Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced-manufactured sensors for measuring peak irradiation temperature within a nuclear test reactor. Although methods of real-time temperature monitoring, such as thermocouples, may be used, the complexity of feedthroughs and attachments to collect real-time measurements greatly increases the cost of the experiment. Instead, passive monitoring techniques may be used for peak- temperature measurement that exploit the melting point of well-characterized materials (standard melt wires) to infer peak reactor temperatures. However, limited available space for instrumentation during experiments introduces an additional challenge. To accommodate this, INL has expanded its temperature- detection instrumentation capabilities to include advanced manufactured (AM) melt wires for peak irradiation temperature measurements. These melt wires can determine peak temperatures while also accommodate space limitations in irradiation experiments. In an effort to improve performance reliability of AM meltwire capabilities, a process was developed and tested to identify the significance of entrapping a high purity inert atmosphere within the packaging of printed melt wire arrays. The materials used in this study were aluminum, zinc, and tin encapsulated in high purity helium within a stainless steel (SS) 316 container. Tin, with a low melting point of approximately 230°C, Zn with a mid-melting point of approximately 420°C, and Al with a high melting point of approximately 660°C. This report describes the design, fabrication process, furnace testing and X-ray Computed Tomography (XCT) evaluation. Results show a successful outcome in creating an inert gas encapsulation and high-resolution evaluation methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Optimal Upsetting of Composite Cylinders

The upsetting of a composite cylinder in frictionless creep is analyzed in energy terms. Three loading programs that lead to upsetting of the cylinder by the same amount in the same time are compared. Variational analysis indicates that the kinematic loading program is best for industrial use.

42 ENGINEERING↗
DOE OSTI2018

SRNL 2017 LDRD Annual Report

Fiscal Year (FY) 2017 marked the twelfth year of Savannah River National Laboratory’s (SRNL’s) Laboratory Directed Research and Development (LDRD) program and the largest year to date. When the program began in FY06, just two years after SRNL becoming the DOE’s 17th national lab, it began with a modest authorized collection rate of just under 1.5%. It grew from there each year, reaching 4% in FY11. FY17 marked the first increase in the authorized collection rate since FY11, increasing from 4% to 5%. This increase highlights the value placed on this important program, which provides the discretionary funding needed to develop ideas for future programmatic growth in our key business areas of environmental stewardship, national security, nuclear materials management, and secure energy manufacturing, as well as develop and maintain our core competencies. The LDRD program leverages the unique capabilities of SRNL to yield foundational scientific research and development (R&D) essential to our core business areas, while aligning optimally and continuously with SRNL’s Strategic Plan and providing long-term benefits to DOE and the National Nuclear Security Administration (NNSA), other customers, and stakeholders. As can be seen in the “By the Numbers” summary, this program also plays an important role in sustaining staff excellence with high participation by students, post-docs, and early-career staff. Over 75% of the projects in FY17 included contributions from early-career or post-doctoral staff.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

2018 LDRD annual report

Fiscal Year (FY) 2018 marked the thirteenth year of Savannah River National Laboratory’s (SRNL’s) Laboratory Directed Research and Development (LDRD) program and the largest year to date. When the program began in FY06, just two years after SRNL becoming the DOE’s 17th national lab, it began with a modest authorized collection rate of just under 1.5%. It grew from there each year, reaching 5% in FY17. The authorized collection rate was maintained at 5% on FY18. The increase in collection rates experienced during the last years evidences the value placed on this important program, which provides the discretionary funding needed to develop ideas for future programmatic growth in our key business areas of environmental stewardship, national security, nuclear materials management, and secure energy manufacturing, as well as develop and maintain our core competencies. The LDRD program leverages the unique capabilities of SRNL to yield foundational scientific research and development (R&D) essential to our core business areas, while aligning optimally and continuously with SRNL’s Strategic Plan and providing long-term benefits to DOE and the National Nuclear Security Administration (NNSA), other customers, and stakeholders. As can be seen in the “By the Numbers” summary, this program also plays an important role in sustaining staff excellence with high participation by students, post-docs, and early-career staff. Over 85% of the projects in FY18 included contributions from early-career or post-doctoral staff.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Improving experimental phases for strong reflections prior to density modification

A genetic algorithm has been developed to optimize the phases of the strongest reflections in SIR/SAD data. This is shown to facilitate density modification and model building in several test cases. Experimental phasing of diffraction data from macromolecular crystals involves deriving phase probability distributions. These distributions are often bimodal, making their weighted average, the centroid phase, improbable, so that electron-density maps computed using centroid phases are often non-interpretable. Density modification brings in information about the characteristics of electron density in protein crystals. In successful cases, this allows a choice between the modes in the phase probability distributions, and the maps can cross the borderline between non-interpretable and interpretable. Based on the suggestions by Vekhter [Vekhter (2005 ▶), Acta Cryst. D61, 899–902], the impact of identifying optimized phases for a small number of strong reflections prior to the density-modification process was investigated while using the centroid phase as a starting point for the remaining reflections. A genetic algorithm was developed that optimizes the quality of such phases using the skewness of the density map as a target function. Phases optimized in this way are then used in density modification. In most of the tests, the resulting maps were of higher quality than maps generated from the original centroid phases. In one of the test cases, the new method sufficiently improved a marginal set of experimental SAD phases to enable successful map interpretation. A computer program, SISA, has been developed to apply this method for phase improvement in macromolecular crystallography.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Comparison of Fuel Loading Pattern Optimization Results using Exhaustive Search for Fresh Fuels and Local Search for Burned Fuels

Fuel loading pattern optimization is a combinatorial optimization problem from the mathematical point of view. However, exhaustive search is impractical because enormous computational cost is necessary due to huge search space. For this reason, various optimization methods have been developed so far, but no methods are able to guarantee to reach the optimum solution. In the present study, search space is divided into subspaces using key parameters that well represent characteristics of a loading pattern and then promising subspaces are extensively searched. In the present method, balance between division of a search space into subspaces and a search method used in a subspace is essential. For example, when the number of division is set to be one, it is equivalent to the exhaustive search thus is impractical. Similarly, when the division to subspaces is too coarse, an optimum (or near optimum) solution cannot be found through local search in each subspace since design space of a subspace is still too large. Contrary, if division to subspaces is too fine, number of subspaces becomes to large thus promising subspaces cannot be found. In the present study, search space is divided focusing on the location of fresh fuel assemblies with gadolinia since it has significant impact on core characteristics. Number of possible loading patterns of gadolinia bearing fresh fuel assemblies is not very large thus exhaustive search is possible. Local fine search is executed by considering location of burned fuels for each loading pattern of gadolinia bearing fresh fuel assembly. If the same solution is obtained by multiple local searches starting from different initial burnt fuel locations (but the same loading patterns for gadolinia bearing fresh fuel assemblies), the obtained solution is highly likely the optimum in the subspace. In more general, the present method is based on the 'divide and conquer' approach, which is familiar in the optimization field. Namely, after division of search space into subspaces and then promising subspace is searched. Once the promising subspace is found, the subspace is further subdivided into subspaces and repeat the above procedures. This method would be efficient when appropriate balance between search space division method and performance of local search is achieved. The previous works utilizes the similar approach. In reference [1], exhaustive search in small region in a core is carried out focusing on loading patterns of gadolinia bearing fresh fuel assemblies. In reference [2], rough exhaustive search based on the 'batch-wise' loading pattern is performed with the assist of the linear programming. By contrast, in the present study, exhaustive search is carried out for loading patterns of gadolinia bearing fresh fuel assemblies. The random Multi-start Local Search (MSLS) with the direct search or the exhaustive binary swap is used to find out optimal solutions in a subspace, which consists of loading patterns having the same arrangement of gadolinia bearing fresh fuel assemblies. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2017

Exploiting Vector and Multicore Parallelsim for Recursive, Data- and Task-Parallel Programs

Modern hardware contains parallel execution resources that are well-suited for data-parallelism-vector units-and task parallelism-multicores. However, most work on parallel scheduling focuses on one type of hardware or the other. In this work, we present a scheduling framework that allows for a unified treatment of task- and data-parallelism. Our key insight is an abstraction, task blocks, that uniformly handles data-parallel iterations and task-parallel tasks, allowing them to be scheduled on vector units or executed independently as multicores. Our framework allows us to define schedulers that can dynamically select between executing task- blocks on vector units or multicores. We show that these schedulers are asymptotically optimal, and deliver the maximum amount of parallelism available in computation trees. To evaluate our schedulers, we develop program transformations that can convert mixed data- and task-parallel pro- grams into task block-based programs. Using a prototype instantiation of our scheduling framework, we show that, on an 8-core system, we can simultaneously exploit vector and multicore parallelism to achieve 14×-108× speedup over sequential baselines.

Ren, Bin↗