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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

MODELING AND OPTIMIZATION STUDY OF A TIGHTLY INTEGRATED ROTARY ELECTRIC MOTOR-HYDRAULIC PUMP

To meet the growing trend of electrification of mechanical systems, this paper presents a compactly integrated electric motor and hydraulic pump. The proposed application for this machine requires high flow rates at low pressure differentials and four quadrant operation. The hydraulic pump architecture selected for this machine is a radial ball piston pump. An inside impinged version of this architecture allows for efficient filling of the chambers and is radial balanced, both of which allow high- speed operation for increased power density. The radial ball piston pump is less expensive to manufacture and is radially more compact than a standard radial cylindrical piston pump. A model of the pump and the integrated electric motor have been created to study scaling relationships and drive detailed design and optimization. The scaling study considers how displacement is affected by pump diameter, and how the diameter and required torque change with angular velocity. The detailed model considers the effect of valve timing, piston-cylinder clearance, and pump geometry on the efficiency. The model is then exercised in an optimization of the machine parameters.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Extended Finite Element Based Approach in Additive Manufacturing Modeling for Optimizing Highly Complex Manifold in Protonic Ceramic Electrochemical Cells

The objective of this project is to develop a novel numerical approach based on the extended finite element method (XFEM) for modeling moving boundaries of material deposited in additive manufacturing (AM) process with improved accuracy and reduced computational cost. One of the major challenges in simulating AM processes is that the boundaries of the computational domain need to change as material is deposited. Previous approaches have achieved this by activating new finite elements, but this requires a high level of mesh refinement to capture small movement of the boundary. XFEM allows the solution boundary to move independently of the mesh, permitting smooth representation of the evolution of the boundary as material is deposited. As a result, this new method will significantly improve the simulation accuracy while reducing the computational cost compared with existing approaches. Furthermore, this project will bring significant improvements for the design and AM process optimization in an iterative fashion among numerical simuation, parameter optimization, and experimental validation. Specifically, this project will support the protonic ceramic electrochemical cells (PCEC) stack development at INL by providing insights into the density, residual stress, thermal and mechanical properties of the PCEC manifold and interconnect, which is critical in enhancing the system lifetime and reducing the overall cost. Upon the success of proposed development and validation of the proposed approach, simulation will be applied to determine an optimal set of AM process parameters for the PCEC manifold production. This project will consolidate Idaho National Laboratory (INL)’s simulation capabilities provided by the MOOSE framework and the Valhalla AM simulation application, with encouraging expansion to emerging PCEC applications and AM technologies at INL and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Simultaneous Process and Heat Exchanger Network Synthesis Using a Discrete Temperature Grid

We present mixed-integer linear programming models for utility targeting and heat exchanger network synthesis that account for variable stream temperatures and flow rates as well as unclassified streams and thus can be combined with process synthesis optimization models. By projecting temperature information onto a fixed discrete temperature grid, the model remains linear even under variable temperatures and flow rates, leading to a more tractable optimization model. The proposed model is well suited to address problems in the area of process intensification where the close interaction between different phenomena naturally leads to process synthesis problems with unclassified process streams. Several extensions are presented, including nonisothermal mixing as well as phase changes. Moreover, we show how a nonuniform grid can be adopted to reduce the complexity of the model for medium and large-size problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Geomagnetic field modeling by optimal recursive filtering

Data sets selected for mini-batches and the software modifications required for processing these sets are described. Initial analysis was performed on minibatch field model recovery. Studies are being performed to examine the convergence of the solutions and the maximum expansion order the data will support in the constant and secular terms.

Source record↗
DOE OSTI2021

Linearized modeling and optimization of shared mooring systems

Shared mooring systems, where adjacent platforms are tethered directly to each other, can reduce anchor quantities and mooring line lengths in a floating array but also introduce new modeling and design challenges. This paper presents and demonstrates a first-order approach to modeling and designing shared mooring systems that simplifies these challenges. We formulate a general, linearized model for the force-displacement response of shared mooring systems, including inter-platform couplings. Using this linearization, we realize significant simplifications to the shared mooring system design problem and propose a corresponding design optimization approach. Finally, we demonstrate the complete approach on a variety of shared-mooring floating wind farm layouts with generic design assumptions and constraints. Some fundamental observations about shared mooring systems can be made from the results. Polygonal array layouts with perpendicular anchor positions appear to maximize shared mooring system efficiency. Also, arrays with greater levels of sharing tend to exhibit larger deviations in offsets between platforms. Overall, the results show how the linearized approach can be applied to efficiently evaluate and systematically optimize preliminary shared mooring system designs.

17 WIND ENERGY↗
DOE OSTI2015

Rigorous force field optimization principles based on statistical distance minimization

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. We exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of the approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modelling and Optimizing CSP-PV Hybrid Systems Using the Hybrid Optimization and Performance Platform (HOPP)

The hybridization of concentrating solar power (CSP) with thermal energy storage (TES), photovoltaics (PV), and electrochemical battery energy storage systems (BESS) has the potential to provide continuous, high-capacity-factor energy production at a lower cost than a PV-BESS or CSP with TES alone. Because of the system complexity of CSP technology, it is challenging to evaluate the technological and financial performance of a CSP-PV hybrid system without detailed modeling of annual operations. To address this challenge, we have developed a modeling framework for evaluating the performance and financial viability of CSP systems hybridized with PV and BESS technologies. This modeling effort incorporates CSP tower and trough configurations into an existing modeling tool recently developed by NREL, the Hybrid Optimization and Performance Platform (HOPP). This paper provides a brief overview of our methodology, as well as an example case study. CSP with TES hybridized with PV provides the best benefit-to-cost ratio compared to the other simulated technology combinations. However, for the conditions considered, this configuration only increases the benefit-to-cost ratio by about 1% compared to the CSP with TES configuration. The PV-BESS system provides the lowest benefit-to-cost ratio compared to the other configurations explored because of the relatively low capacity credit received by the system.

Hamilton, William (ORCID:0000000244654239)↗
DOE OSTI2015

Robust PET-guided intensity-modulated radiation therapy

Purpose: Functional image guided intensity-modulated radiation therapy has the potential to improve cancer treatment quality by basing treatment parameters such as heterogeneous dose distributions information derived from imaging. However, such heterogeneous dose distributions are subject to imaging uncertainty. In this paper, the authors develop a robust optimization model to design plans that are desensitized to imaging uncertainty. Methods: Starting from the pretreatment fluorodeoxyglucose-positron emission tomography scans, the authors use the raw voxel standard uptake values (SUVs) as input into a series of intermediate functions to transform the SUV into a desired dose. The calculated desired doses were used as an input into a robust optimization model to generate beamlet intensities. For each voxel, the authors assume that the true SUV cannot be observed but instead resides in an interval centered on the nominal (i.e., observed) SUV. Then the authors evaluated the nominal and robust solutions through a simulation study. The simulation considered the effect of the true SUV being different from the nominal SUV on the quality of the treatment plan. Treatment plans were compared on the metrics of objective function value and tumor control probability (TCP). Results: Computational results demonstrate the potential for improvements in tumor control probability and deviation from the desired dose distribution compared to a nonrobust model while maintaining acceptable tissue dose. Conclusions: Robust optimization can help design treatment plans that are more stable in the presence of image value uncertainties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Block-oriented modeling of superstructure optimization problems

We present a novel software framework for modeling large-scale engineered systems as mathematical optimization problems. A key motivating feature in such systems is their hierarchical, highly structured topology. Existing mathematical optimization modeling environments do not facilitate the natural expression and manipulation of hierarchically structured systems. Rather, the modeler is forced to "flatten" the system description, hiding structure that may be exploited by solvers, and obfuscating the system that the modeling environment is attempting to represent. To correct this deficiency, we propose a Python-based "block-oriented" modeling approach for representing the discrete components within the system. Our approach is an extension of the Pyomo library for specifying mathematical optimization problems. Through the use of a modeling components library, the block-oriented approach facilitates a clean separation of system superstructure from the details of individual components. This approach also naturally lends itself to expressing design and operational decisions as disjunctive expressions over the component blocks. By expressing a mathematical optimization problem in a block-oriented manner, inherent structure (e.g., multiple scenarios) is preserved for potential exploitation by solvers. In particular, we show that block-structured mathematical optimization problems can be straightforwardly manipulated by decomposition-based multi-scenario algorithmic strategies, specifically in the context of the PySP stochastic programming library. We illustrate our block-oriented modeling approach using a case study drawn from the electricity grid operations domain: unit commitment with transmission switching and N - 1 reliability constraints. Finally, we demonstrate that the overhead associated with block-oriented modeling only minimally increases model instantiation times, and need not adversely impact solver behavior. (C) 2013 Elsevier Ltd. All rights reserved.

Friedman, Z↗
DOE OSTI2016

Robust Ceramic Coaxial Cable Down-Hole Sensors for Long-Term In Situ Monitoring of Geologic CO 2 Injection and Storage

Energy generation by use of fossil fuels produces large volumes of CO 2 and other greenhouse gases, whose accumulation in the atmosphere is widely seen as undesirable. CO 2 Capture followed by sequestration has been identified as the solution. Subsurface geologic formations offer a potential location for long-term storage of CO 2 because of their requisite size. Unfortunately, the inaccessibility and complexity of the subsurface, the wide range of scales of variability, and the coupled nonlinear processes, impose tremendous challenges to determine the transport and predict the fate of the stored CO 2 . Among the various monitoring approaches, in situ down-hole monitoring of the various state parameters provides critical and direct data points that can be used to validate the models, optimize the injection, detect leakage and track the CO 2 plume. However, down-hole sensors that can withstand the harsh conditions and operate over decades of the project lifecycle remain unavailable. Given that the widespread of carbon capture and storage will be the necessity and reality in the future, fundamental and applied research is required to address the significant challenges and technological gaps in lack of long-term reliable down-hole sensors This project focused on the development and demonstration of a novel, low-cost, distributed, robust ceramic coaxial cable sensor platform for in situ down-hole monitoring of geologic CO 2 injection and storage with high spatial and temporal resolutions. The coaxial cable Fabry-Perot interferometer (CCFPI) has been studied as a general sensor platform for in situ, long-term, measurement of temperature, pressure and strain, which are critical to CO 2 injection and storage. A novel signal processing scheme has been developed and demonstrated for dense multiplexing of the sensors for low-cost distributed sensing with high spatial resolution. The developed temperature, pressure and strain sensors have been extensively tested under laboratory conditions that are similar to the downhole CO 2 storage environment, showing excellent capability for in situ monitoring the various parameters that are important to model, optimize the injection, detect leakage and track the CO 2 plume. In addition, the interactions between the sensor datum and the geological models have been investigated in details for the purposes of model validation, guiding sensor installation/placement, enhancement of model prediction capability and optimization of the injection processes. This project has resulted in the successful development of new ceramic coaxial cable based sensor systems that can monitor directly the changes in pressure, temperature, and strain caused by increased reservoir pressure and reduced reservoir temperature due to the supercritical CO 2 injection. Integrated with geological models, the sensors and measurement data can improve the possibility to identify plume movement and leakage in the cap rock and wells with higher precision and more accuracy. The low cost, ease of deployment, small size and dense multiplexing features of the new sensing technology will allow a large number of sensors to be deployed to address the objective to demonstrate that 99% of the CO 2 remains in the injection zone.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Cost Optimization of Osmotically Assisted Reverse Osmosis

We develop a nonlinear optimization model to identify minimum cost designs for osmotically assisted reverse osmosis (OARO), a multistaged membrane-based process for desalinating high-salinity brines. The optimization model enables comprehensive evaluation of a complex process configuration and operational decision space that includes nonlinear process performance and implicit relationships among membrane stages, saline sweep cycles, and makeup, purge, and recycle streams. Here, the objective function minimizes cost, rather than energy or capital expenditures, to accurately account for the trade-offs in capital and operational expenses inherent in multistaged membrane processes. Generally, we find that cost-optimal OARO processes minimize the number of stages, eliminate the use of saline makeup streams, purge from the first sweep cycle, and successively decrease stage membrane area and sweep flow rates. The optimal OARO configuration for treating feed salinities of 50–125 g/L total dissolved solids with water recoveries between 30–70% results in costs less than or equal to $6 per m3 of product water. Sensitivity analysis suggests that future research to minimize OARO costs should focus on minimizing the membrane structural parameter while maximizing the membrane burst pressure and reducing the membrane unit cost.

42 ENGINEERING↗
DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Development of Reduced Glass Furnace Model to Optimize Process Operation

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and PPG Industries to develop a reduced-order glass furnace model that enables plant operators to make informed, realtime process adjustments. This CRADA project is sponsored under the High-Performance Computing for Manufacturing (HPC4Mfg) Program of the Department of Energy's Advanced Manufacturing Office (AMO) within the Energy Efficiency and Renewable Energy (EERE) Office. This program ran from June 2016 to July 2017 with no extensions.

42 ENGINEERING↗
NASA NTRS1984

Model for optimal parallax in stereo radar imagery

Simulated stereo radar imagery is used to investigate parameters for a spaceborne imaging radar. Incidence angles ranging from small to intermediate to large are used with three digital terrain model areas which are representative of relatively flat, moderately rough, and mountaneous terrain. The simulated radar imagery was evaluated by interpreters for ease of stereo perception and information content, and rank ordered within each class of terrain. The interpreter's results are analyzed for trends between the height of a feature and either parallax or vertical exaggeration for a stereo pair. A model is developed which predicts the amount of parallax (or vertical exaggeration) an interpreter would desire for best stereo perception of a feature of a specific height. Results indicate the selection of angle of incidence and stereo intersection angle depend upon the relief of the terrain. Examples of the simulated stereo imagery are presented for a candidate spaceborne imaging radar having four selectable angles of incidence.

Pisaruck, M. A.↗
NASA NTRS1997

The Microgravity Vibration Isolation Mount: A Dynamic Model for Optimal Controller Design

Vibration acceleration levels on large space platforms exceed the requirements of many space experiments. The Microgravity Vibration Isolation Mount (MIM) was built by the Canadian Space Agency to attenuate these disturbances to acceptable levels, and has been operational on the Russian Space Station Mir since May 1996. It has demonstrated good isolation performance and has supported several materials science experiments. The MIM uses Lorentz (voice-coil) magnetic actuators to levitate and isolate payloads at the individual experiment/sub-experiment (versus rack) level. Payload acceleration, relative position, and relative orientation (Euler-parameter) measurements are fed to a state-space controller. The controller, in turn, determines the actuator currents needed for effective experiment isolation. This paper presents the development of an algebraic, state-space model of the MIM, in a form suitable for optimal controller design.

Hampton, R. David↗
DOE OSTI2017

Development of Reduced Glass Furnace Model to Optimize Process Operation (Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and PPG Industries to develop a reduced-order glass furnace model that enables plant operators to make informed, real time process adjustments. This CRADA project is sponsored under the High-Performance Computing for Manufacturing (HPC4Mfg) Program of the Department of Energy's Advanced Manufacturing Office (AMO) within the Energy Efficiency and Renewable Energy (EERE) Office. This program ran from June 2016 to July 2017 with no extensions.

42 ENGINEERING↗
DOE OSTI2020

Online Modelling and Optimization of Nonlinear Integrable Systems

Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Reactive Dye Adsorption from Aqueous Solution on HPEI-Modified Fe{sub 3}O{sub 4} Nanoparticle as a Superadsorbent: Characterization, Modeling, and Optimization

In this work, the optimum condition for the adsorption of Reactive Blue 19 dye onto Fe{sub 3}O{sub 4} functionalized with hyperbranched polyethylenimine (Fe@HPEI) was determined using response surface methodology. The properties of synthesized magnetic Fe@HPEI nanoparticles were ascertained using FTIR, SEM, TEM, VSM and zeta potential measurement. The experimental adsorption data indicating the positive effect of adsorbent dose and contact time, and the negative effect of pH, initial dye concentration and ionic strength on dye adsorption. At optimal condition; pH 3.4, contact time 25 min, adsorbent dose 0.4 g/L and initial dye concentration 113 mg/L, Fe@HPEI nanoparticles removed dye up to 99.27%. The maximum experimental dye adsorption was near to the predicted value of 100%, which confirmed the reliability of the selected statistical model. The dye adsorption data were fitted well to Langmuir isotherm and pseudo-second-order kinetic, and the maximum adsorption capacity was 500 mg/g. Totally, Fe@HPEI as an effective superadsorbent can be repeatedly utilized for the adsorption of dye from the aqueous environment.

77 NANOSCIENCE AND NANOTECHNOLOGY↗