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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The CPAT 2.0.2 Domain Model - How CPAT 2.0.2 "Thinks" From an Analyst Perspective.

To help effectively plan the management and modernization of their large and diverse fleets of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) and the Program Executive Office Combat Support and Combat Service Support (PEO CS &CSS) commissioned the development of a large - scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets. This report, which is an update to the original CPAT domain model published in 2015 (SAND2015 - 4009), covers important new CPAT features. This page intentionally left blank

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The CPAT Domain Model - How CPAT "Thinks" from an Analyst Perspective

To help effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A Correction to the Stress–Strain Curve During Multistage Hot Deformation of 7150 Aluminum Alloy Using Instantaneous Friction Factors

Multistage stress–strain curve correction based on an instantaneous friction factor was studied for axisymmetric uniaxial hot compression of 7150 aluminum alloy. Experimental friction factors were calculated based on continuous isothermal axisymmetric uniaxial compression tests at various deformation parameters. Then, an instantaneous friction factor equation was fitted by mathematic analysis. After verification by comparing single-pass flow stress correction with traditional average friction factor correction, the instantaneous friction factor equation was applied to correct multistage stress–strain curves. The corrected results were reasonable and validated by multistage relative softening calculations. This research provides a broad potential for implementing axisymmetric uniaxial compression in multistage physical simulations and friction optimization in finite element analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗
DOE OSTI2016

Augmenting the one-shot framework by additional constraints

The (multistep) one-shot method for design optimization problems has been successfully implemented for various applications. To this end, a slowly convergent primal fixed-point iteration of the state equation is augmented by an adjoint iteration and a corresponding preconditioned design update. In this paper we present a modification of the method that allows for additional equality constraints besides the usual state equation. Finally, a retardation analysis and the local convergence of the method in terms of necessary and sufficient conditions are given, which depend on key characteristics of the underlying problem and the quality of the utilized preconditioner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Co-optimization of Heavy-Duty Fuels and Engines: Cost Benefit Analysis and Implications

Heavy-duty vehicles require expensive after treatment systems for control of emissions such as particulate matter (PM) and nitrogen oxides (NOx) to comply with stringent emission standards. Reduced engine-out emissions could potentially alleviate the emission control burden, and thus bring about reductions in the cost associated with after treatment systems, which translates into savings in vehicle ownership. This study evaluates potential reductions in manufacturing and operating costs of redesigned emission after treatment systems of line-haul heavy-duty diesel vehicles (HDDVs) with reduced engine-out emissions brought about by co-optimized fuel and engine technologies. Three emissions reduction cases representing conservative, medium, and optimistic engine-out emission reduction benefits are analyzed, compared to a reference case: the total costs of after treatment systems (TCA) of the three cases are reduced to 11,400(1.63 cents/km), 9,100 (1.30 cents/km), and 8,800 (1.26 cents/km), respectively, compared to 12,000 (1.71 cents/km) for the reference case. The largest potential reductions result from reduced diesel exhaust fluid (DEF) usage due to lower NOx emissions. Downsizing after treatment devices is not likely, because the sizes of devices are dependent on not only engine-out emissions, but also other factors such as engine displacement. As a result, sensitivity analysis indicates that the price and usage of DEF have the largest impacts on TCA reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2018

Evaluation of Engineered Barrier Systems in the Disposition of Spent Nuclear Fuel

This document is a summary of the R&D activities associated with the Engineered Barrier Systems Work Package. Multiple facets of Engineered Barrier Systems (EBS) research were examined in the course of FY18 activities. This report is focused on delivering an update on the status and progress of modelling tools and experimental methods, both of which are essential to understanding and predicting long-term repository performance as part of the safety case. Specifically, the work described herein aims to improve understanding of EBS component evolution and interactions. Utlimately, the EBS Work Package is working towards producing process models for distinct processes that can either be incorporated into performance assessment (PA), or provide critical information for implementing better constraints on barrier performance The main objective of this work is that the models being developed and refined will either be implemented directly into the Generic Disposal System Analysis platform (GDSA), or can otherwise be indirectly linked to the performance assessment by providing improved bounding conditions. In either the case, the expectation is that validated modelling tools will be developed that provide critical input to the safety case. This report covers a range of topics — modelling topics include: thermal-hydrologic-mechanical-chemical coupling (THMC) in buffer materials, comparisons of modelling approaches to optimize computational efficiency, thermal analysis for EBS/repository design, benchmarking of thermal analysis tools, and a preliminary study of buffer re-saturation processes. Experimental work reported, includes: chemical evolution and sorption behavior of clay-based buffer materials and high-pressure, high temperature studies of EBS material interactions. The work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. This includes participation in the HotBENT Field Test, aimed at understanding near-field effects on EBS materials at temperatures above 100 °C, and the analysis of data and characterization of samples from the FEBEX Field Test. Both the FEBEX and HotBENT Field Tests utilize/utilized the Grimsel Test Site in Switzerland, which is situated in a granite host rock. These tests offer the opportunity to understand near field evolution of bentonite buffer at in situ conditions for either a relatively long timescale (18 years for FEBEX) or temperature above 100 °C (HotBENT). Overall, this report provides in depth descriptions of tools and capabilities to investigate nearfield performance of EBS materials (esp. bentonite buffer), as well as tools for drift-scale thermal and thermal-hydrologic analysis critical to EBS and repository design. For a more detailed description of work contained herein, please see Section 10 ("Conclusions") of this document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Optimizing group structures using hierarchical division

Creating group structures with few groups that give low errors is a difficult problem in reactor analysis. In recent years, automated optimization techniques have been applied to this task. We continue this trend by applying the hierarchical division algorithm to generate optimized group structures that minimize a cost function. At each stage, the algorithm adds a single group boundary to an existing group structure, dividing one group into two to increase the resolution of the group structure. The location of the added boundary is the one that gives the lowest error over all possible new boundary locations. Our implementation requires a beginning group structure, a set of candidate new boundary locations, and a set of reference reaction rates. As a proof of concept, we used WIMS-69 as the initial group structure, XMAS-172 as the ending group structures, and a 344-group reference group structure. Testing on two simple, homogenized reactor problems, we found that hierarchical division was able to reduce the error by a factor of around 5 with an increase of only 15% in the number of groups. Because hierarchical division can get stuck in local minima, it often reaches a plateau in its error reduction capability as many groups are added. Nevertheless, we find hierarchical division has strong potential to make good group structures into great group structures at a modest increase in computational cost. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Toward high-throughput defect density quantification: A comparison of techniques for irradiated samples

Transmission electron microscopy (TEM) is an established tool used for the investigation of defects in materials. Traditionally, diffraction contrast techniques—two-beam bright-field and weak-beam dark-field—have been used to image defects due to contrast sensitivity from weak lattice strains. Use of these methods entail an intricate tilt series of imaging using different diffracting vectors, g, to verify the g•b invisibility criterion relative to the different defect types and habit planes inherent to the material. Recently, the addition of down-zone imaging and STEM imaging has also proven to be effective imaging techniques for defect density analysis. Interest in nanocrystalline (NC) materials, spurred by their conjectured superior properties compared to their coarse-grain counterparts, has been thriving and the investigation of their defect morphologies is essential. Maneuvering within NC samples in the TEM adds another layer of difficulty making the aforementioned techniques not practical for application to specimens with complex microstructures. For this reason, we have devised a protocol for identifying NC grains optimally oriented for quantitative analysis using NanoMegas ASTAR automated crystal orientation mapping (ACOM) in the TEM. In this work, we conduct a series of experiments assessing the effectiveness of conventional two-beam bright-field, weak-beam dark-field, and down-zone STEM imaging. Here, we also evaluate an ACOM-assisted multibeam imaging method and compare defect density results obtained using each technique in an irradiated nanocrystalline Au sample.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Optimal investment strategies for distributed generation in distribution networks with real option analysis

Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology.

Zou, Bo↗
DOE OSTI2021

Optimal Planning and Operation of Multi-Frequency HVac Transmission Systems

Low-frequency high-voltage alternating-current (LF-HVac) transmission scheme has been recently proposed as an alternative solution to conventional 50/60-Hz HVac and high-voltage direct-current (HVdc) schemes for bulk power transfer. This paper proposes an optimal planning and operation for loss minimization in a multi-frequency HVac transmission system. In such a system, conventional HVac and LF-HVac grids are interconnected using back-to-back (BTB) converters. The dependence of system MW losses on converter dispatch as well as the operating voltage and frequency in the LF-HVac is discussed and compared with that of HVdc transmission. Based on the results of the loss analysis, multi-objective optimization formulations for both planning and operation stages are proposed. The planning phase decides a suitable voltage level for the LF-HVac grid, while the operation phase determines the optimal operating frequency and power dispatch of BTB converters, generators, and shunt capacitors. A solution approach that effectively handles the variations of transmission line parameters with the rated voltage and operating frequency in the LF-HVac grid is proposed. The proposed solutions of the planning and operation stages are evaluated using a multi-frequency HVac system. The results show a significant loss reduction and improved voltage regulation during a 24-hour simulation.

Nguyen, Quan H.↗
DOE OSTI2021

Demonstration of Dimensional and Microstructural Postirradiation Examination Capabilities on MiniFuel Disk Specimens

This work demonstrates two new MiniFuel postirradiation examination (PIE) capabilities, laser profilometry and serial sectioning, for future use on alpha-U specimens for the upcoming Mini-99 irradiation campaign. Representative U–Mo disk specimens—3 mm in diameter and approximately 0.75 mm in thickness—were subjected to dimensional measurements using standard calipers, as well as a new Keyence CL-3000 series laser profilometry system designed to measure the surface of both sides of the disk simultaneously. The laser profilometry system captured thickness gradients across the specimens as large as 0.2 mm. Additionally, because of the limitations inherent to the use of calipers, caliper measurements consistently overestimated the initial volume of MiniFuel disk specimens compared to the laser profilometry method by up to 5%. However, the results show that optimal data collection and analysis procedures are crucial to avoiding significant artifacts during data collection that can lead to large errors (approximately 10%). Serial sectioning using a combination of ion milling and electron imaging was also demonstrated for the analysis of micron-sized control volumes. Nanometer- to micron-scale porosity was resolved on a representative U–Mo specimen before irradiation, even when the surface of the specimen appeared free of porosity. Future efforts to relate microstructural features (porosity, fission product distributions, and so on) to macroscale effects (dimensional changes, thermal conductivity degradation) require accurate characterization methods such as those demonstrated in this work. These advanced PIE capabilities will be applied to optimized alpha-U specimens for the upcoming Mini-99 neutron irradiation campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2017

The Salem Smart Power Center: An Assessment of Battery Performance and Economic Potential

This paper presents an assessment of the economic potential of a 5 MW/1.25 MWh Energy Storage System (ESS) installed at the Salem Smart Power Center (SSPC), a smart grid technology demonstration facility owned and operated by Portland General Electric (PGE) in Salem, Oregon. The ESS and the grid conditions in which it operates were modeled using Pacific Northwest National Laboratory’s Battery Storage Evaluation Tool (BSET) to explore tradeoffs between services, and to develop optimal control strategies. This assessment monetized the value derived from nine services the SSPC could provide to PGE and the customers it serves. The ESS and the grid conditions in which it operates were modeled using PNNL’s in-house optimization tool BSET to explore tradeoffs between services, and to develop optimal control strategies. The analysis resulted in a number of lessons that provide crucial insights into the practical application of ESS, including; The SSPC, which was originally conceived as a research and test facility and built with the prevailing maturity technology level, was built at a cost ($20.4 million) that exceeds current day prices ($5.4 million) for a similarly designed and built 5 MW/1.25 MWh system; In terms of economic operation, the SSPC is currently underutilized, deployed only for primary frequency response. PNNL modeling indicates that optimal operation of the ESS could generate an additional value of $2.3 million over 20 years. It should also be noted that primary frequency response is the highest benefit application but requires a response from the SSPC only 17 hours each year. While optimally engaged, the ESS would provide arbitrage and ancillary services 78 percent of the time, but those services generate only 27 percent of the total value; Participation in Western EIM represents an interesting opportunity for PGE with a potential to generate $2.1 million value in PV terms over 20 years in the 5-min real-time market; With an energy to power ratio of only 0.25, the SSPC is not well suited to engage in most energy-intensive applications such as arbitrage or ancillary services. By upsizing the storage capacity to 5 MWh and 10MWh, the additional value allows the benefits ($13.3 million and 20.3 million, respectively) to exceed the system’s revenue requirements ($11.5 and $16.4 million, respectively). For the SSPC, ROI ratios exceeded 1.0 when the energy to power ratio fell between 0.5 and 3.5, and peaked at an energy to power ratio of 2.0. This report represents the output of the first of a two-phase effort. Phase II will involve the development of enhanced control strategies to assist PGE in realizing the benefits of energy storage in real-time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A New Approach to Predict Hydrogeological Parameters Using Shear Waves from the Multichannel Analysis of Surface Waves Method

For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( V s ) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated V s and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.

Engineering↗