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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Energy Saving Melting and Revert Reduction Technology (E-SMARRT): Design Support for Tooling Optimization

High pressure die casting is an intrinsically efficient net shape process and improvements in energy efficiency are strongly dependent on design and process improvements that reduce scrap rates so that more of the total consumed energy goes into acceptable, usable castings. Computer simulation has become widely used within the industry but use is not universal. Further, many key design decisions must be made before the simulation can be run and expense in terms of money and time often limits the number of decision iterations that can be explored. This work continues several years of work creating simple, very fast, design tools that can assist with the early stage design decisions so that the benefits of simulation can be maximized and, more importantly, so that the chances of first shot success are maximized. First shot success and better running processes contributes to less scrap and significantly better energy utilization by the process. This new technology was predicted to result in an average energy savings of 1.83 trillion BTUs/year over a 10 year period. Current (2011) annual energy saving estimates over a ten year period, based on commercial introduction in 2012, a market penetration of 30% by 2015 is 1.89 trillion BTUs/year by 2022. Along with these energy savings, reduction of scrap and improvement in yield will result in a reduction of the environmental emissions associated with the melting and pouring of the metal which will be saved as a result of this technology. The average annual estimate of CO2 reduction per year through 2022 is 0.037 Million Metric Tons of Carbon Equivalent (MM TCE).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Radically Engineered Modular Air Separation System with Tailored Oxygen Sorbents

The commercial energy sector relies heavily on fossil fuel conversion, and in the process releases a significant amount of CO 2 . A promising technology to utilize fossil fuels with relatively affordable CO 2 capture is gasification. However, it requires a pure oxygen stream. The current state of the art method to produce oxygen is cryogenic air separation, which supercools air to a liquid, and then using distillation columns to separate the components. While this method has been thoroughly studied, it only has a 25% efficiency from a second law standpoint and therefore requires a significant amount of energy (and associated emissions) for oxygen production. This, then, lowers the incentive for carbon capture within a plant, so alternative methods need to be investigated. One potential method, chemical looping air separation (CLAS), is a promising method to replace state of the art oxygen generation technologies. CLAS utilizes a cyclic redox scheme with an oxygen sorbent to create pure oxygen streams. This approach typically utilizes a dual reactor scheme where the oxygen deficient sorbent enters the first reactor and is subjected to high oxygen partial pressures to re-oxidize the sorbent. Then the sorbent is sent to the reducing reactor, where it is subjected to low oxygen partial pressure (steam or vacuum) to releases oxygen. The overarching objective of this project was to discover the principles for rational design and optimization of oxygen sorbents and process design to ensure the process is a viable replacement for cryogenic air separation, especially in the context of modular gasification systems. This was done through development, characterization, testing, and analyses of (a) high temperature mixed composite oxides; (b) low temperature doped perovskite oxides (A1 x A2 1-x B1 y B2 1-y O 3 ); (c) scale up synthesis and testing of the optimized sorbent particles; (d) process design and analyses of the CLAS system in the context of modular gasification applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

The partial reduction of clean and doped alpha-Fe2O3(0001) from first principles

Fe surfaces have a wide range of applications due to the ability of Fe to be present in a number of different oxidation states, which can be further influenced by the presence of dopants that change the energetic and electronic surface properties. Thus, having an understanding of the elementary reduction processes of clean Fe oxide surfaces, and how dopants affect the energetics and electronics of such processes, is crucial to the science-guided design and optimization of said surfaces. In this work, we use density functional theory (DFT) to investigate the partial reduction of a-Fe2O3(0001) in the absence and presence of Pd and Pt dopants. In order to account for the strong on-site Coulombic interactions present in transition metal oxides, we used both the DFT?+?U method and 2006 Heyd-Scuseria-Ernzerhof functional and compare the results obtained from the different methods. We find that oxygen vacancies created in the clean a-Fe2O3(0001) surface prefer to diffuse into a subsurface layer, with the charge left behind from the first surface oxygen vacancy distributing between all of the near surface Fe atoms. The addition of Pd or Pt to the a-Fe2O3(0001) surface significantly decreases the energy required to create the first surface oxygen vacancy, suggesting the dopants intrinsically promote the reduction of a-Fe2O3(0001). Additionally, the presence of the dopants caused the charge left behind by the creation of the first surface oxygen vacancy to be localized on the dopant itself as well as the highest Fe atom. Overall, this work provides insight into the elementary energetic and electronic properties of the fully oxidized and partially reduced a-Fe2O3(0001) surface in the absence and presence of Pd and Pt dopants, which has potential implications for the design and optimization of doped Fe oxide surfaces.

Hensley, Alyssa↗
DOE OSTI2016

Minimally Mutated HIV-1 Broadly Neutralizing Antibodies to Guide Reductionist Vaccine Design

An optimal HIV vaccine should induce broadly neutralizing antibodies (bnAbs) that neutralize diverse viral strains and subtypes. However, potent bnAbs develop in only a small fraction of HIV-infected individuals, all contain rare features such as extensive mutation, insertions, deletions, and/or long complementarity-determining regions, and some are polyreactive, casting doubt on whether bnAbs to HIV can be reliably induced by vaccination. We engineered two potent VRC01-class bnAbs that minimized rare features. According to a quantitative features frequency analysis, the set of features for one of these minimally mutated bnAbs compared favorably with all 68 HIV bnAbs analyzed and was similar to antibodies elicited by common vaccines. This same minimally mutated bnAb lacked polyreactivity in four different assays. We then divided the minimal mutations into spatial clusters and dissected the epitope components interacting with those clusters, by mutational and crystallographic analyses coupled with neutralization assays. Finally, by synthesizing available data, we developed a working-concept boosting strategy to select the mutation clusters in a logical order following a germline-targeting prime. We have thus developed potent HIV bnAbs that may be more tractable vaccine goals compared to existing bnAbs, and we have proposed a strategy to elicit them. This reductionist approach to vaccine design, guided by antibody and antigen structure, could be applied to design candidate vaccines for other HIV bnAbs or protective Abs against other pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

An optimization method for flexural bearing design for high-stroke high-frequency applications

A method for optimization of a high-stroke, high-frequency flexural bearing was completed. The main goal was to design a flexural bearing system as the energy restoration component of a one kW free piston linear engine alternator (LEA) operating at 90 Hz and maximum targeted displacement of 22 mm. Defined electricity output of 1 kW, with more than 30% efficiency, required engine operation under high speed and high stroke conditions. Under such challenging stroke and frequency requirements, a method of design was required to select between a wide range of parameters in flexural bearing design. A parametric CAD model was prepared and used in the optimization package of a finite element analysis (FEA) software. Four main parameters of flexural bearing geometry were identified and included the outer diameter, thickness, number of arms, and sweep angle of spiral cuts. Other design parameters including spiral gap width, shape factor and spiral start points angle were shown to be less important compared to these four main parameters in primary design and were examined separately. A neural network algorithm was used to investigate the interactions and effects of design parameters. The sensitivity of each parameter on output criteria such as maximum stress and natural frequency was examined and design charts developed. Methods for further modification of the spiral curve at end points were introduced to reduce the maximum stress level by up to 20%. Several steel alloys and a titanium-based alloy were identified as proper material candidates for high cycle applications and the final designed spring was manufactured and tested to validate the FEA results of axial stiffness and strain level throughout the flexure arms. The percent of the flexure’s moving mass was estimated to be 29% with FEA analysis and compared well with the experimental value of 31%. The stress/strain results of the FEA analysis matched with the experimental results – within ±5%.

42 ENGINEERING↗
DOE OSTI2015

Computational modeling of Krypton gas puffs with tailored mass density profiles on Z

Large diameter multi-shell gas puffs rapidly imploded by high current (~20 MA, ~100 ns) on the Z generator of Sandia National Laboratories are able to produce high-intensity Krypton K-shell emission at ~13 keV. Efficiently radiating at these high photon energies is a significant challenge which requires the careful design and optimization of the gas distribution. To facilitate this, we hydrodynamically model the gas flow out of the nozzle and then model its implosion using a 3-dimensional resistive, radiative MHD code (GORGON). This approach enables us to iterate between modeling the implosion and gas flow from the nozzle to optimize radiative output from this combined system. Furthermore, guided by our implosion calculations, we have designed gas profiles that help mitigate disruption from Magneto-Rayleigh–Taylor implosion instabilities, while preserving sufficient kinetic energy to thermalize to the high temperatures required for K-shell emission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Status of the Super-B factory Design

The SuperB international team continues to optimize the design of an electron-positron collider, which will allow the enhanced study of the origins of flavor physics. The project combines the best features of a linear collider (high single-collision luminosity) and a storage-ring collider (high repetition rate), bringing together all accelerator physics aspects to make a very high luminosity of 10{sup 36} cm{sup -2} sec{sup -1}. This asymmetric-energy collider with a polarized electron beam will produce hundreds of millions of B-mesons at the Y(4S) resonance. The present design is based on extremely low emittance beams colliding at a large Piwinski angle to allow very low {beta}{sub y} without the need for ultra short bunches. Use of crab-waist sextupoles will enhance the luminosity, suppressing dangerous resonances and allowing for a higher beam-beam parameter. The project has flexible beam parameters, improved dynamic aperture, and spin-rotators in the Low Energy Ring for longitudinal polarization of the electron beam at the Interaction Point. Optimized for best colliding-beam performance, the facility may also provide high-brightness photon beams for synchrotron radiation applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Cogging Torque Minimization in Transverse Flux Machines

This paper presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method. A three-level DOE is performed to reach an optimum solution with minimum simulations. Finite element analysis is used to study the different effects. Two prototypes are being fabricated for experimental verification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

The BLAIRR Irradiation Facility Hybrid Spallation Target Optimization

BLAIRR STUDY STATUS OVERVIEW Beamline Complex Evaluation/Assessment and Adaptation to the Goals Facility Radiological Constraints ? Large scale analyses of conventional facility and integrated shield (concrete, soil)Target Optimization and Design: Beam-target interaction optimization Hadronic interaction and energy deposition limitations Single phase and Hybrid target concepts Irradiation Damage Thermo-mechanical considerations Spallation neutron fluence optimization for (a) fast neutron irradiation damage (b) moderator/reflector studies, (c) NTOF potential and optimization (d) mono-energetic neutron beam

36 MATERIALS SCIENCE↗
DOE OSTI2020

Simultaneous material, shape and topology optimization

Using three design fields we develop an optimization environment that can simultaneously optimize material, shape and topology. We use the implicit representation of the boundaries with level-set functions that define the shape and topology. Differentiable R-functions allow us to combine these shapes and topology descriptions with Boolean operations. Additionally, we incorporate design dependent-stiffness materials with another design field. Notably, this framework accommodates design dependent loads, has the ability to introduce holes, and ensures the satisfaction of optimality criteria. It builds upon the fictitious domain, ersatz material, material interpolation and level-set methods. Additionally, it also borrows from parameterized density-based topology optimization methods. Since analytical sensitivities can be computed, we use efficient nonlinear programming algorithms to update the design instead of the Hamilton–Jacobi’s scheme of level-set methods. We illustrate the features of our framework by designing a cantilever beam with octet truss microlattice, a dam with design-dependent loads, and a composite clevis plate.

42 ENGINEERING↗
DOE OSTI2020

Production Through Simulation: Using Simulation Technologies to Create and Evaluate Nuclear Fuel Fabrication Facility Designs

This project explores how physics-based and discrete-event simulation technologies can be jointly employed to model a suite of nuclear fuel fabrication facility designs with variable radiological environments, optimize their design to lower operational costs subject to a set of design constraints, and evaluate their performance. Initially, three variants of U-20Pu-10Zr metallic nuclear fuel in three specific geometric configurations are modeled and studied across more than 50 physics simulations in nuclear physics software packages SCALE and Monte Carlo N-Particle Transport (MCNP6.2). From these tests, values for effective dose rate and criticality are recorded for each of the nine alloy-geometry configurations. These values are then incorporated into discrete-event models of fuel fabrication in the simulation program ExtendSim Pro 10 as process attributes. This allows one to track the dose to facility personnel as fuel is fabricated, dually enabling us to evaluate the safety of a given facility design in terms of absorbed dose per worker per year and to craft operational guidelines that adhere to federal and local safety regulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Optimizing neural networks

A system and method design and optimize neural networks. The system and method include a data store that stores a plurality of gene vectors that represent diverse and distinct neural networks and an evaluation queue stored with the plurality of gene vectors. Secondary nodes construct, train, and evaluate the neural network and automatically render a plurality of fitness values asynchronously. A primary node executes a gene amplification on a select plurality of gene vectors, a crossing-over of the amplified gene vectors, and a mutation of the crossing-over gene vectors automatically and asynchronously, which are then transmitted to the evaluation queue. The process continuously repeats itself by processing the gene vectors inserted into the evaluation queue until a fitness level is reached, a network's accuracy level plateaus, a processing time period expires, or when some stopping condition or performance metric is met or exceeded.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Cogging torque minimization in transverse flux machines

This study presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method. A multi-level DOE is proposed as an optimization method to reach an optimum solution with minimum simulations. The case study is analyzed in a two level DOE optimization. Finite element analysis is used to study the different effects. Two prototypes are fabricated for validating the FEA results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Simplified Building Modeling Approach for Identifying Whole Building Retrofits

Building Energy Modeling (BEM) is an effective strategy for optimizing new and retrofit building design, evaluating its energy savings potential and rating its energy performance. However, because of the complexity and expense of modeling, only a small percentage of buildings are simulated. Over 80% of buildings are <25,000 ft2, and the cost associated with BEM can be a big deterrent for reaching this subsector, which usually ends up complying with energy codes and qualifying for incentives using prescriptive measures or generalized design guides. Similarly, efficiency considerations for small building retrofits are often identified through deemed measures or Technical Reference Manuals, which though a scalable and convenient approach, can limit innovation in design and optimization of measures. Simplification of the modeling process, where appropriate, can lead to increased energy savings and more informed decision making. This paper will discuss the development of a ruleset for a simplified approach based on the Performance Rating Method (PRM) contained in ASHRAE Standard 90.1. The approach aims to simplify and lower the cost of the energy modeling process in a controlled and documented manner. An anticipated outcome is an increased use of BEM and its effectiveness in the design, retrofit and operation of commercial buildings. In addition, simplification of the modeling process will result in fewer errors from misinterpretations of both program requirements and program intent in simulation software. The paper will discuss the need and advantages of a simplified modeling approach for whole building retrofits and the technical process for achieving the same.

Goel, Supriya↗
DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗
DOE OSTI2024

High-dimensional control co-design of a wave energy converter with a novel pitch resonator power takeoff system

Researchers are exploring adding wave energy converters to existing oceanographic buoys to provide a predictable source of renewable power. A ”pitch resonator” power take-off system has been developed that generates power using a geared flywheel system designed to match resonance with the pitching motion of the buoy. However, the novelty of the concept leaves researchers uncertain about various design aspects of the system. This work presents a novel design study of a pitch resonator to inform design decisions for an upcoming deployment of the system. The assessment uses control co-design via WecOptTool to optimize control trajectories for maximal electrical power production while varying five design parameters of the pitch resonator. Given the large search space of the problem, the control trajectories are optimized within a Monte Carlo analysis to identify optimal designs, followed by parameter sweeps around the optimum to identify trends between the design parameters. The gear ratio between the pitch resonator spring and flywheel are found to be the most sensitive design variables to power performance. Finally, the assessment also finds similar power generation for various sizes of resonator components, suggesting that correctly designing for optimal control trajectories at resonance is more critical to the design than component sizing.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗