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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fast Tuning-Free Distributed Algorithm for Solving the Network-Constrained Economic Dispatch

With the increasing penetration of distributed energy resources (DERs) and their participation in the electricity market, it becomes more desirable to apply distributed algorithms for resource allocation in order to address the resulting computational and communicational challenges. Most of the existing distributed algorithms for solving the network-constrained economic dispatch (NCED) problem require the tuning of certain auxiliary parameters. As a result, the robustness of these algorithms against the varieties in DERs is greatly undermined. In this paper, a new distributed algorithm, optimality condition consensus (OCC), is proposed to solve the NCED problem by using distributed power flow (DPF) and ratio consensus as fundamental tools. It inherits the advantages of existing distributed algorithms for the NCED problem but removes the need for parameter tuning to improve performance in practice. In conclusion, the effectiveness of the proposed distributed algorithm in terms of efficiency, scalability, and robustness is demonstrated through detailed case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations

The recovery of approximately sparse or compressible coefficients in a polynomial chaos expansion is a common goal in many modern parametric uncertainty quantification (UQ) problems. However, relatively little effort in UQ has been directed toward theoretical and computational strategies for addressing the sparse corruptions problem, where a small number of measurements are highly corrupted. Such a situation has become pertinent today since modern computational frameworks are sufficiently complex with many interdependent components that may introduce hardware and software failures, some of which can be difficult to detect and result in a highly polluted simulation result. In this paper we present a novel compressive sampling--based theoretical analysis for a regularized $\ell^1$ minimization algorithm that aims to recover sparse expansion coefficients in the presence of measurement corruptions. Our recovery results are uniform (the theoretical guarantees hold for all compressible signals and compressible corruptions vectors) and prescribe algorithmic regularization parameters in terms of a user-defined a priori estimate on the ratio of measurements that are believed to be corrupted. We also propose an iteratively reweighted optimization algorithm that automatically refines the value of the regularization parameter and empirically produces superior results. Lastly, our numerical results test our framework on several medium to high dimensional examples of solutions to parameterized differential equations and demonstrate the effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Matlab Code for Multiscale Topology Optimization

This Matlab script implements a simple topology optimization algorithm that minimizes the compliance of a structure within a 3D prismatic design domain. It uses a simple linear elastic finite element solver to solve the equilibrium equations, and the optimality criterion (00) method to update the design iteratively. This functionality is identical to, or the simplest possible extension from 2D to 3D of, a 99-line Matlab code released in 2001 in a journal article by Sigmund. What is new in this code is our implementation of simple surrogate models for several truss micro-architectures, which converts this code to a multiscale design tool, whereas the original code is single-scale. The development of these surrogate models and their implementation is described in a journal article we intend to submit. We plan to include the full source code in an Appendix in this paper. The code’s primary function is as a pedagogical tool rather than as a finished software product. Sigmund’s 2001 paper describes the implementation line-by-line. We are including this updated version of the code to allow the reader to understand exactly what changes must be made to a “standard” topology optimization code to implement the surrogate models and thus multiscale design optimization.

Watts, Seth↗
DOE OSTI2018

Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations

The recovery of approximately sparse or compressible coefficients in a polynomial chaos expansion is a common goal in many modern parametric uncertainty quantification (UQ) problems. However, relatively little effort in UQ has been directed toward theoretical and computational strategies for addressing the sparse corruptions problem, where a small number of measurements are highly corrupted. Such a situation has become pertinent today since modern computational frameworks are sufficiently complex with many interdependent components that may introduce hardware and software failures, some of which can be difficult to detect and result in a highly polluted simulation result. In this paper we present a novel compressive sampling-based theoretical analysis for a regularized t1 minimization algorithm that aims to recover sparse expansion coefficients in the presence of measurement corruptions. Our recovery results are uniform (the theoretical guarantees hold for all compressible signals and compressible corruptions vectors), and prescribe algorithmic regularization parameters in terms of a user-defined a priori estimate on the ratio of measurements that are believed to be corrupted. We also propose an iteratively reweighted optimization algorithm that automatically refines the value of the regularization parameter, and empirically produces superior results. Our numerical results test our framework on several medium-to-high dimensional examples of solutions to parameterized differential equations, and demonstrate the effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Transport Analysis & Optimization in a MW-Scale CO2 Electrolyzer (Final Report)

As Twelve continues to scale up their CO2 electrolyzers, both in the size of a single cell and in the number of cells used in a stack, thermal management becomes a growing concern, since excess heat can affect reaction yield and accelerate degradation. In this project, we aim to computationally explore how the anode flow fields used in Twelve’s CO2 electrolyzers function as heat exchangers. In particular, using a homogenized model of a CO2 electrolyzer, we first estimate the amount of heat generated in a cell. Then, we develop a computational fluid dynamics (CFD) model of the so-called “flow field”, i.e. a flow manifold, based on Twelve’s CAD drawings, to evaluate how these flow fields perform as a heat exchanger for the generated heat. We explore both a single cell and a 3-cell stack operating in parallel, where heat generated in one cell can now be transferred to another cell. We evaluate how performance is affected when environmental heat losses are taken into account. Finally, we leverage topology optimization to explore the types of design features a computational optimization algorithm would suggest to supplement our intuition. Overall, our work aims to provide design recommendations for CO2 electrolyzer flow fields and provides a foundation for future studies of flow field optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Dual structural-control optimization of large space structures

A new approach is proposed for solving dual structural-control optimization problems for high-order flexible space structures where reduced-order structural models are employed. For a given initial structural dessign, a quadratic control cost is minimized subject to a constant-mass constraint. The sensitivity of the optimal control cost with respect to the stuctural design variables is then determined and used to obtain successive structural redesigns using a contrained gradient optimization algorithm. This process is repeated until the constrained control cost sensitivity becomes negligible. A numerical example is presented which demonstrates that this new approach effectively addresses the problem of dual optimization for potentially very high-order structures.

Messac, A.↗
NASA NTRS2014

Three-Dimensional Trajectory Design for Reducing Climate Impact of Trans-Atlantic Flights

The impact of aircraft emissions and contrails on the environment adds an additional aspect to aircraft trajectory optimization. This study developed a three-dimensional trajectory optimization algorithm for trans-Atlantic flights in cruise to generate aircraft trajectories that minimize environmental impacts due to CO2 emissions and contrails in the presence of winds. The climate-optimal trajectory is developed using dynamic programming that adjusts a wind-optimal aircraft heading while determining the optimal locations, altitudes and times for en-route step climbs. Flying wind-optimal routes minimize aircraft travel time, fuel burn and associated emissions during cruise while adjusting aircraft heading and en-route step climbs at the optimal locations and times minimize climate impact of contrails. This capability integrates an air traffic management simulation with aircraft fuel burn and emissions models, contrail formation and dispersion models, simplified climate response models, and a common climate metric. A study was conducted to evaluate the potential cost and benefits of flying climate-optimal routes in North Atlantic Airspace and their impacts to the Organized Track System design based on the trans-Atlantic air traffic during a day, July 12, 2012. Results show eastbound flights achieved a larger environmental benefit with less additional fuel burn than westbound flights that operated in strong headwinds that caused more additional fuel burn and aircraft emissions to avoid traversing contrails favorable regions.

climate impact of aviation↗
DOE OSTI2016

Technical Note: SPEKTR 3.0—A computational tool for x-ray spectrum modeling and analysis

Purpose: A computational toolkit (SPEKTR 3.0) has been developed to calculate x-ray spectra based on the tungsten anode spectral model using interpolating cubic splines (TASMICS) algorithm, updating previous work based on the tungsten anode spectral model using interpolating polynomials (TASMIP) spectral model. The toolkit includes a MATLAB (The Mathworks, Natick, MA) function library and improved user interface (UI) along with an optimization algorithm to match calculated beam quality with measurements. Methods: The SPEKTR code generates x-ray spectra (photons/mm{sup 2}/mAs at 100 cm from the source) using TASMICS as default (with TASMIP as an option) in 1 keV energy bins over beam energies 20–150 kV, extensible to 640 kV using the TASMICS spectra. An optimization tool was implemented to compute the added filtration (Al and W) that provides a best match between calculated and measured x-ray tube output (mGy/mAs or mR/mAs) for individual x-ray tubes that may differ from that assumed in TASMICS or TASMIP and to account for factors such as anode angle. Results: The median percent difference in photon counts for a TASMICS and TASMIP spectrum was 4.15% for tube potentials in the range 30–140 kV with the largest percentage difference arising in the low and high energy bins due to measurement errors in the empirically based TASMIP model and inaccurate polynomial fitting. The optimization tool reported a close agreement between measured and calculated spectra with a Pearson coefficient of 0.98. Conclusions: The computational toolkit, SPEKTR, has been updated to version 3.0, validated against measurements and existing models, and made available as open source code. Video tutorials for the SPEKTR function library, UI, and optimization tool are available.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Validation and Demonstration of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an integrated energy system (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. The new RAVEN plugin for grid and capacity optimization (HERON) only addresses the limits that affect the production variables and the corresponding rates of variation (explicit constraints). However, other variables are subject to constraints, and the associated limits should be accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response affects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. To obtain a more accurate approximation of the nonlinear dynamic behavior, a parametric version of the dynamic mode decomposition with control (DMDc) algorithm was developed to derive the state-space representation matrices of the IES components at different scheduling parameter. Thanks to this approach, a more accurate approximation of the system response can be obtained, the limits imposed by thermal mechanical implicit constraints can be translated into power dispatch limits, and the feedbacks to HERON power dispatcher can be provided. To assess the developed methodology, a power dispatching test case composed of three power generating and storage units (Balance of Plant, Secondary Energy Source, Thermal Energy Storage) was developed. The power output of each one of the three units was optimized to meet the imposed time-dependent load demand trajectory and to maximize the IES profitability by meeting both the explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy

Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation of additional modules to include any aspect of the treatment process, and therefore has great potential for both assessment and optimization within radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Automated Dynamic Demand Response Implementation on a Micro-grid

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2 R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2 R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability to dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2 R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.

Kuppannagari, Sanmukh R.↗
NASA NTRS1981

The controlled growth method - A tool for structural optimization

An adaptive design variable linking scheme in a NLP based optimization algorithm is proposed and evaluated for feasibility of application. The present scheme, based on an intuitive effectiveness measure for each variable, differs from existing methodology in that a single dominant variable controls the growth of all others in a prescribed optimization cycle. The proposed method is implemented for truss assemblies and a wing box structure for stress, displacement and frequency constraints. Substantial reduction in computational time, even more so for structures under multiple load conditions, coupled with a minimal accompanying loss in accuracy, vindicates the algorithm.

Hajela, P.↗
NASA NTRS1991

Optimal aircraft performance during microburst encounter

The effects of microburst characteristics on the optimal penetration performance of jet transport and general aviation aircraft are presented. The purpose is to determine the best possible performance that can be achieved in a broad range of microbursts. A secondary goal is to illustrate good strategies for dealing with a range of microbursts during takeoff and landing. Over 1100 optimal trajectories were computed for two aircraft types flying through idealized microbursts using a Successive Quadratic Programs trajectory optimization algorithm. Contours of safety metrics are plotted as functions of the length scales, magnitudes, and locations of horizontal wind shears and vertical downdrafts. These performance contours show three length-scale regimes for optimal microburst penetration. At short length scales, hazards usually associated with gustiness predominate (e.g., high normal load factor, rotational upset). At intermediate length scales, a degraded ability to maintain flight path and/or vertical velocity poses the most serious threat. At very long microburst length scales, excessive touchdown velocities may result. The ability to transit a microburst successfully also varies strongly with microburst location. The results show that both aircraft types could penetrate some very severe microbursts if optimal control histories were followed. Nevertheless, these control strategies assume perfect prior knowledge of the wind, and practical limits to successful encounter with real-time control capabilities would be lower. The optimally controlled jet transport can successfully penetrate higher intensity microbursts than can the general aviation aircraft.

Psiaki, Mark L.↗
NASA NTRS1996

Experimental Optimization Methods for Multi-Element Airfoils

A modern three element airfoil model with a remotely activated flap was used to investigate optimum flap testing position using an automated optimization algorithm in wind tunnel tests. Detailed results for lift coefficient versus flap vertical and horizontal position are presented for two angles of attack: 8 and 14 degrees. An on-line first order optimizer is demonstrated which automatically seeks the optimum lift as a function of flap position. Future work with off-line optimization techniques is introduced and aerodynamic hysteresis effects due to flap movement with flow on are discussed.

Landman, Drew↗
NASA NTRS2012

Apparatus and methods for manipulation and optimization of biological systems

The invention provides systems and methods for manipulating, e.g., optimizing and controlling, biological systems, e.g., for eliciting a more desired biological response of biological sample, such as a tissue, organ, and/or a cell. In one aspect, systems and methods of the invention operate by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system, e.g., a bioreactor. In alternative aspects, systems include a device for sustaining cells or tissue samples, one or more actuators for stimulating the samples via biochemical, electromagnetic, thermal, mechanical, and/or optical stimulation, one or more sensors for measuring a biological response signal of the samples resulting from the stimulation of the sample. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The compositions and methods of the invention can be used, e.g., to for systems optimization of any biological manufacturing or experimental system, e.g., bioreactors for proteins, e.g., therapeutic proteins, polypeptides or peptides for vaccines, and the like, small molecules (e.g., antibiotics), polysaccharides, lipids, and the like. Another use of the apparatus and methods includes combination drug therapy, e.g. optimal drug cocktail, directed cell proliferations and differentiations, e.g. in tissue engineering, e.g. neural progenitor cells differentiation, and discovery of key parameters in complex biological systems.

Ho, Chih-Ming↗
NASA NTRS1974

Study of genetic direct search algorithms for function optimization

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Zeigler, B. P.↗
DOE OSTI2019

Mixing state evolution of agglomerating particles in an aerosol chamber: Comparison of measurements and particle-resolved simulations

This article introduces a validation study of the stochastic particle-resolved aerosol model PartMC with experimental data from an aerosol chamber experiment. For the experiment, a scanning mobility particle sizer and a single-particle soot photometer were used to monitor the aerosol mixing state evolution of two initially externally-mixed aerosol populations of ammonium sulfate and black carbon particles undergoing agglomeration. We applied an efficient optimization algorithm (ProSRS) to determine several unconstrained simulation parameters and were able to successfully reproduce number concentrations and size distributions of mixed particles that formed by agglomeration. The PartMC modeling method in conjunction with the optimization procedure provides a tool for detailed comparisons of chamber experiments and modeling, where aerosol mixing state is the focus of investigation.

54 ENVIRONMENTAL SCIENCES↗