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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing Distribution Grid Resilience Through Model Predictive Controller Enabled Prioritized Load Restoration Strategy

Effective resilience improvement strategies enable the power grid to cope with disruptive extreme events. Most power grid outages are caused by disruptions in distribution grids. Motivated by the urgent need for power system resilience research, this paper proposes a priority-weighted optimal load restoration technique to enhance the resilience of distribution grids against extreme events. The proposed technique is based on smart distribution technology and framed as sequential multi-step decision process (MDP) and mixed integer linear program (MILP). It is formulated as optimal control problem with a model predictive control (MPC) approach. We applied the devised MILP-MPC-based load restoration technique to a simplified single-bus version of the IEEE 13-bus distribution system with integrated distributed energy resources (DERs) such as wind turbine, photovoltaic array, microturbine, and energy storage device. The technique executes a reducing and rolling horizon optimization in each control step in real-time using the forecasted information of the renewables, the fuel status of the microturbine and the state of charge of the energy storage device. We consider an extreme event which triggered outage of the upstream utility grid and caused islanded operation of the distribution grid. We demonstrated the effectiveness of the proposed MPC approach in restoring the distribution grid loads based on their priority during the main grid outage-caused islanded operation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

A multi-objective programming model for assessment the GHG emissions in MSW management

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Proactive Operations and Investment Planning via Stochastic Optimization to Enhance Power Systems’ Extreme Weather Resilience

We present scalable stochastic optimization approaches for improving power systems’ resilience to extreme weather events. We consider both proactive redispatch and transmission line hardening as alternatives for mitigating expected load shed due to extreme weather, resulting in large-scale stochastic linear programs (LPs) and mixed-integer linear programs (MILPs). We solve these stochastic optimization problems with progressive hedging (PH), a parallel, scenario-based decomposition algorithm. Our computational experiments indicate that our proposed method for enhancing power system resilience can provide high-quality solutions efficiently. With up to 128 scenarios on a 2,000-bus network, the operations (redispatch) and investment (hardening) resilience problems can be solved in approximately 6 min and 2 h of wall-clock time, respectively. Additionally, we solve the investment problems with up to 512 scenarios, demonstrating that the approach scales very well with the number of scenarios. Moreover, the method produces high quality solutions that result in statistically significant reductions in expected load shed. Our proposed approach can be augmented to incorporate a variety of other operational and investment resilience strategies, or a combination of such strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1983

Computer programs for generation and evaluation of near-optimum vertical flight profiles

Two extensive computer programs were developed. The first, called OPTIM, generates a reference near-optimum vertical profile, and it contains control options so that the effects of various flight constraints on cost performance can be examined. The second, called TRAGEN, is used to simulate an aircraft flying along an optimum or any other vertical reference profile. TRAGEN is used to verify OPTIM's output, examine the effects of uncertainty in the values of parameters (such as prevailing wind) which govern the optimum profile, or compare the cost performance of profiles generated by different techniques. A general description of these programs, the efforts to add special features to them, and sample results of their usage are presented.

Sorensen, J. A.↗
NASA NTRS1984

STAEBL: Structural tailoring of engine blades, phase 2

The Structural Tailoring of Engine Blades (STAEBL) program was initiated at NASA Lewis Research Center in 1980 to introduce optimal structural tailoring into the design process for aircraft gas turbine engine blades. The standard procedure for blade design is highly iterative with the engineer directly providing most of the decisions that control the design process. The goal of the STAEBL program has been to develop an automated approach to generate structurally optimal blade designs. The program has evolved as a three-phase effort with the developmental work being performed contractually by Pratt & Whitney Aircraft. Phase 1 was intended as a proof of concept in which two fan blades were structurally tailored to meet a full set of structural design constraints while minimizing DOC+I (direct operating cost plus interest) for a representative aircraft. This phase was successfully completed and was reported in reference 1 and 2. Phase 2 has recently been completed and is the basis for this discussion. During this phase, three tasks were accomplished: (1) a nonproprietary structural tailoring computer code was developed; (2) a dedicated approximate finite-element analysis was developed; and (3) an approximate large-deflection analysis was developed to assess local foreign object damage. Phase 3 is just beginning and is designed to incorporated aerodynamic analyses directly into the structural tailoring system in order to relax current geometric constraints.

Hirschbein, M. S.↗
NASA NTRS1992

An interactive system for aircraft design and optimization

A system for aircraft design utilizing a unique analysis architecture, graphical interface, and suite of numerical optimization methods is described in this paper. The non-procedural architecture provides extensibility and efficiency not possible with conventional programming techniques. The interface for analysis and optimization, developed for use with this method, is described and its application to example problems is discussed.

Kroo, Ilan M.↗
NASA NTRS1998

The Design and Evaluation of "CAPTools"--A Computer Aided Parallelization Toolkit

Writing applications for high performance computers is a challenging task. Although writing code by hand still offers the best performance, it is extremely costly and often not very portable. The Computer Aided Parallelization Tools (CAPTools) are a toolkit designed to help automate the mapping of sequential FORTRAN scientific applications onto multiprocessors. CAPTools consists of the following major components: an inter-procedural dependence analysis module that incorporates user knowledge; a 'self-propagating' data partitioning module driven via user guidance; an execution control mask generation and optimization module for the user to fine tune parallel processing of individual partitions; a program transformation/restructuring facility for source code clean up and optimization; a set of browsers through which the user interacts with CAPTools at each stage of the parallelization process; and a code generator supporting multiple programming paradigms on various multiprocessors. Besides describing the rationale behind the architecture of CAPTools, the parallelization process is illustrated via case studies involving structured and unstructured meshes. The programming process and the performance of the generated parallel programs are compared against other programming alternatives based on the NAS Parallel Benchmarks, ARC3D and other scientific applications. Based on these results, a discussion on the feasibility of constructing architectural independent parallel applications is presented.

Yan, Jerry↗
DOE OSTI2016

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2010

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

Ng, Hok K.↗
NASA NTRS1978

A study of some economic factors relating to the development and implementation of a satellite power system

Areas are examined relating to the design, development and implementation of a satellite power system (SPS): an analysis of the effect of energy R&D programs in general and SPS in particular on optimal fossil fuel consumption patterns, a study of alternative uses of SPS technologies, and a study of the electric power market penetration potential for SPS. It is shown that a credible program of R&D on long-range energy alternatives leads to lower optimal prices for fossil fuels, resulting in large short-term benefits accruing to the specific program elements. Several alternative uses of SPS technologies were identified; however the markets for these technologies are generally quite diffuse and difficult to assess. The notable exception is solar array technology which has, potentially, a very large non-SPS market. It is shown that the market for SPS units derives from two components of demand: the demand created by growth in the electrical energy demand which leads to an increased demand for baseload generating capacity, and a demand created by the need to replace retiring capacity.

Source record↗
DOE OSTI2018

Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming

Autonomous vehicle technology provides the means to optimize motion planning beyond human capacity. In particular, the problem of navigating multi-lane traffic optimally for trip time, energy efficiency, and collision avoidance presents challenges beyond those of single-lane roadways. For example, the host vehicle must simultaneously track multiple obstacles, the drivable region is non-convex, and automated vehicles must obey social expectations. Furthermore, reactive decision-making may result in becoming stuck in an undesirable traffic position. This paper presents a fundamental approach to these problems using model predictive control with a mixed integer quadratic program at its core. Lateral and longitudinal movements are coordinated to avoid collisions, track a velocity and lane, and minimize acceleration. Vehicle-to-vehicle connectivity provides a preview of surrounding vehicles’ motion. Simulation results show a 79% reduction in congestion-induced travel time and an 80% decrease in congestion-induced fuel consumption compared to a rule-based approach.

Dollar, Robert Austin↗
DOE OSTI2015

Barrier elision for production parallel programs

Large scientific code bases are often composed of several layers of runtime libraries, implemented in multiple programming languages. In such situation, programmers often choose conservative synchronization patterns leading to suboptimal performance. In this paper, we present context-sensitive dynamic optimizations that elide barriers redundant during the program execution. In our technique, we perform data race detection alongside the program to identify redundant barriers in their calling contexts; after an initial learning, we start eliding all future instances of barriers occurring in the same calling context. We present an automatic on-the-fly optimization and a multi-pass guided optimization. We apply our techniques to NWChem - a 6 million line computational chemistry code written in C/C++/Fortran that uses several runtime libraries such as Global Arrays, ComEx, DMAPP, and MPI. Our technique elides a surprisingly high fraction of barriers (as many as 63%) in production runs. This redundancy elimination translates to application speedups as high as 14% on 2048 cores. Our techniques also provided valuable insight about the application behavior, later used by NWChem developers. Overall, we demonstrate the value of holistic context-sensitive analyses that consider the domain science in conjunction with the associated runtime software stack.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1985

Transonic rotor tip design using numerical optimization

The aerodynamic design procedure for a new blade tip suitable for operation at transonic speeds is illustrated. For the first time, 3 dimensional numerical optimization was applied to rotor tip design, using the recent derivative of the ROT22 code, program R22OPT. Program R22OPT utilized an efficient quasi-Newton optimization algorithm. Multiple design objectives were specified. The delocalization of the shock wave was to be eliminated in forward flight for an advance ratio of 0.41 and a tip Mach number of 0.92 at psi = 90 deg. Simultaneously, it was sought to reduce torque requirements while maintaining effective restoring pitching moments. Only the outer 10 percent of the blade span was modified; the blade area was not to be reduced by more than 3 percent. The goal was to combine the advantages of both sweptback and sweptforward blade tips. A planform that featured inboard sweepback was combined with a sweptforward tip and a taper ratio of 0.5. Initially, the ROT22 code was used to find by trial and error a planform geometry which met the design goals. This configuration had an inboard section with a leading edge sweep of 20 deg and a tip section swept forward at 25 deg; in addition, the airfoils were modified.

Tauber, Michael E.↗
NASA NTRS1997

COMETBOARDS Can Optimize the Performance of a Wave-Rotor-Topped Gas Turbine Engine

A wave rotor, which acts as a high-technology topping spool in gas turbine engines, can increase the effective pressure ratio as well as the turbine inlet temperature in such engines. The wave rotor topping, in other words, may significantly enhance engine performance by increasing shaft horse power while reducing specific fuel consumption. This performance enhancement requires optimum selection of the wave rotor's adjustable parameters for speed, surge margin, and temperature constraints specified on different engine components. To examine the benefit of the wave rotor concept in engine design, researchers soft coupled NASA Lewis Research Center's multidisciplinary optimization tool COMETBOARDS and the NASA Engine Performance Program (NEPP) analyzer. The COMETBOARDS-NEPP combined design tool has been successfully used to optimize wave-rotor-topped engines. For illustration, the design of a subsonic gas turbine wave-rotor-enhanced engine with four ports for 47 mission points (which are specified by Mach number, altitude, and power-setting combinations) is considered. The engine performance analysis, constraints, and objective formulations were carried out through NEPP, and COMETBOARDS was used for the design optimization. So that the benefits that accrue from wave rotor enhancement could be examined, most baseline variables and constraints were declared to be passive, whereas important parameters directly associated with the wave rotor were considered to be active for the design optimization. The engine thrust was considered as the merit function. The wave rotor engine design, which became a sequence of 47 optimization subproblems, was solved successfully by using a cascade strategy available in COMETBOARDS. The graph depicts the optimum COMETBOARDS solutions for the 47 mission points, which were normalized with respect to standard results. As shown, the combined tool produced higher thrust for all mission points than did the other solution, with maximum benefits around mission points 11, 25, and 31. Such improvements can become critical, especially when engines are sized for these specific mission points.

Patnaik, Surya N.↗
NASA NTRS2007

Reusable, Extensible High-Level Data-Distribution Concept

A framework for high-level specification of data distributions in data-parallel application programs has been conceived. [As used here, distributions signifies means to express locality (more specifically, locations of specified pieces of data) in a computing system composed of many processor and memory components connected by a network.] Inasmuch as distributions exert a great effect on the performances of application programs, it is important that a distribution strategy be flexible, so that distributions can be adapted to the requirements of those programs. At the same time, for the sake of productivity in programming and execution, it is desirable that users be shielded from such error-prone, tedious details as those of communication and synchronization. As desired, the present framework enables a user to refine a distribution type and adjust it to optimize the performance of an application program and conceals, from the user, the low-level details of communication and synchronization. The framework provides for a reusable, extensible, data-distribution design, denoted the design pattern, that is independent of a concrete implementation. The design pattern abstracts over coding patterns that have been found to be commonly encountered in both manually and automatically generated distributed parallel programs. The following description of the present framework is necessarily oversimplified to fit within the space available for this article. Distributions are among the elements of a conceptual data-distribution machinery, some of the other elements being denoted domains, index sets, and data collections (see figure). Associated with each domain is one index set and one distribution. A distribution class interface (where "class" is used in the object-oriented-programming sense) includes operations that enable specification of the mapping of an index to a unit of locality. Thus, "Map(Index)" specifies a unit, while "LocalLayout(Index)" specifies the local address within that unit. The distribution class can be extended to enable specification of commonly used distributions or novel user-defined distributions. A data collection can be defined over a domain. The term "data collection" in this context signifies, more specifically, an abstraction of mappings from index sets to variables. Since the index set is distributed, the addresses of the variables are also distributed.

James, Mark↗