Search NASA⌕ Search

SEARCH · Search NASA

Results for “program optimizations”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

Abstract Background Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models. Results We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions. Availability and implementation Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA Supplementary information Supplementary data are available at Bioinformatics online.

Chan, Siu H. J. (ORCID:000000027707656X)↗
DOE OSTI2017

Microgrid Optimal Scheduling With Chance-Constrained Islanding Capability

To facilitate the integration of variable renewable generation and improve the resilience of electricity sup-ply in a microgrid, this paper proposes an optimal scheduling strategy for microgrid operation considering constraints of islanding capability. A new concept, probability of successful islanding (PSI), indicating the probability that a microgrid maintains enough spinning reserve (both up and down) to meet local demand and accommodate local renewable generation after instantaneously islanding from the main grid, is developed. The PSI is formulated as mixed-integer linear program using multi-interval approximation taking into account the probability distributions of forecast errors of wind, PV and load. With the goal of minimizing the total operating cost while preserving user specified PSI, a chance-constrained optimization problem is formulated for the optimal scheduling of mirogrids and solved by mixed integer linear programming (MILP). Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator and a battery demonstrate the effectiveness of the proposed scheduling strategy. Lastly, we verify the relationship between PSI and various factors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Dynamic Investment and Counterparty Risk

We introduce a dynamic optimization framework in which collateral is used to mitigate losses arising at counterparty’s default. The investor faces two sources of risk: the default risk of the entity referencing the traded credit swap security, and counterparty risk generated from the default event of the trading counterparty. We show that the value function of the control problem coincides with the classical solution of a nonlinear dynamic programming equation. We provide an explicit characterization of the optimal investment strategy, and show that the investor does not trade if counterparty risk is sufficiently high. These findings suggest that moving credit swap trades into well-designed clearinghouses may stimulate economic activities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Optimization problems in natural gas transportation systems. A state-of-the-art review

Our paper provides a review on the most relevant research works conducted to solve natural gas transportation problems via pipeline systems. The literature reveals three major groups of gas pipeline systems, namely gathering, transmission, and distribution systems. In this work, we aim at presenting a detailed discussion of the efforts made in optimizing natural gas transmission lines.There is certainly a vast amount of research done over the past few years on many decision-making problems in the natural gas industry and, specifically, in pipeline network optimization. In this work, we present a state-of-the-art survey focusing on specific categories that include short-term basis storage (line-packing problems), gas quality satisfaction (pooling problems), and compressor station modeling (fuel cost minimization problems). We also discuss both steady-state and transient optimization models highlighting the modeling aspects and the most relevant solution approaches known to date. Although the literature on natural gas transmission system problems is quite extensive, this is, to the best of our knowledge, the first comprehensive review or survey covering this specific research area on natural gas transmission from an operations research perspective. Furthermore, this paper includes a discussion of the most important and promising research areas in this field. Hence, our paper can serve as a useful tool to gain insight into the evolution of the many real-life applications and most recent advances in solution methodologies arising from this exciting and challenging research area of decision-making problems.

03 NATURAL GAS↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
DOE OSTI2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program’s (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States’ largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework’s current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Validation of reference genes for real-time PCR of cord blood mononuclear cells, differentiating endothelial progenitor cells, and mature endothelial cells

Highlights: • RT-qPCR was used to assess and follow the differentiation of EPCs. • A set of reliable and non-variable genes is key to obtain accurate RT-qPCR results. • The relative gene expression of CD34+ and differentiating endothelial progenitor cells was determined. • The optimal set of reference for the gene expression studies performed by RT-qPCR was determined. In the last ten years, endothelial progenitor cells (EPCs) have gained interest as an attractive cell population in regenerative medicine for vascular applications. This population is defined as the precursor of endothelial mature cells (ECs) through a process of differentiation. To our knowledge, no single marker can be used to discriminate them from mature ECs. To effectively study their differentiation kinetics, gene expression must be assessed. Quantitative real-time PCR (RT-qPCR) is widely used to analyze gene expression. To minimize the impact of variances from RT-qPCR, a rigorous selection of reference genes must be performed prior to any experiments due to variations in experimental conditions. In this study, CD34+ mononuclear cells were extracted from human cord blood and differentiated into EPCs after seeding for a maximum period of 21 days. To choose the best combinations of reference genes, we compared the results of EPCs, CD34+ mononuclear cells, and mature endothelial cells to ensure that the differentiation kinetics did not affect the expression of our selected reference genes. The expression levels of seven genes, namely, YWHAZ, GAPDH, HPRT1, RPLP0, UBC, B2M, and TBP were thus compared. The algorithms geNorm, NormFinder, BestKeeper, and the Comparative ΔCt method were employed to assess the expression of each candidate gene. Overall results reveal that the expression stability of reference genes may differ depending on the statistical program used. YWHAZ, GAPDH, and UBC composed the optimal set of reference genes for the gene expression studies performed by RT-qPCR in our experimental conditions. This work can thus serve as a starting point for the selection of candidate reference genes to normalize the levels of gene expression in endothelial progenitor cell populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2023

Optimization Algorithm for Criticality Experiment Design Using Whisper

Many criticality experiments performed to aid in nuclear data evaluation are designed to maximize the sensitivity of the system’s effective neutron multiplication factor to a certain nuclide reaction pair over an energy region of interest. This is typically done by evaluating possible designs in a transport code such as MCNP and selecting the one with the highest desired sensitivity. A designer has many tools to try to maximize this sensitivity such as different moderators, reflectors, fuels, and geometries. This balancing act of identifying a critical and maximally sensitive system become very computationally expensive as more variables are added and higher precisions are desired. In order to identify these optimal configurations more efficiently a Particle Swarm Optimization (PSO) algorithm coupled with MCNP has been developed by Los Alamos National Laboratory (LANL). This algorithm has been used to design two upcoming criticality experiments that will be performed at the National Criticality Experiments Research Center (NCERC), located at the Nevada National Security Site, and operated by LANL, the only general-purpose critical experiments laboratory in the United States. PSO uses a population (swarm) of candidate solutions (particles) on a search space of dimensions such as moderator and reflector thicknesses or enrichments and concentrations. These particles move around the search space from generation to generation according to simple rules. Eventually, the swarm converges on the configuration that is both critical and maximally sensitive to a piece of nuclear data. PSO is well suited for criticality experiments as the algorithm is agnostic to the underlying physics, meaning it is effective on many different experimental setups. This algorithm has been modified to maximize the nuclear data similarity coefficient between an application case and an experiment aimed at replicating the application case using WHISPER, a nuclear criticality safety analysis tool. This allows for the efficient design of critical experiments informed by nuclear data sensitives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

StreamJIT: a commensal compiler for high-performance stream programming

There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A modular GUI-based program for genetic algorithm-based feedback-assisted wavefront shaping

Abstract We have developed a modular graphical user interface (GUI)-based program for use in genetic algorithm-based feedback-assisted wavefront shaping. The program uses a class-based structure to separate out the universal modules (e.g. GUI, multithreading, optimization algorithms) and hardware-specific modules (e.g. code for different SLMs and cameras). This modular design makes the program easily adaptable to a wide range of lab equipment, while providing easy access to a GUI, multithreading, and three optimization algorithms (phase-stepping, simple genetic, and microgenetic).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Microgrid to enable optimal distributed energy retail and end-user demand response

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

ZettaBricks: A Language Compiler and Runtime System for Anyscale Computing

This grant supported the ZettaBricks and OpenTuner projects. ZettaBricks is a new implicitly parallel language and compiler where defining multiple implementations of multiple algorithms to solve a problem is the natural way of programming. ZettaBricks makes algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The ZettaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. Additionally, ZettaBricks introduces novel techniques to autotune algorithms for different convergence criteria. When choosing between various direct and iterative methods, the ZettaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice. OpenTuner is a generalization of the experience gained in building an autotuner for ZettaBricks. OpenTuner is a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Determining Stability Margins in Adiabatic Superconducting Magnets with 3-D Finite Element Analysis

Superconducting magnets play a key role in the development of experiments at Fermilab; understanding the operating stability of these can allow us to utilize more potent magnets for future experiments (like the proposed Muon Collider), optimize the design of magnets in more immediate experiments (like Mu2e), and research the future use of more exotic materials (like high-temperature superconductors). This summer, I developed a 3-D parametric FEA program in ANSYS Mechanical APDL that simulates quench in superconducting magnets, and I also developed a parametric MATLAB program that predicts thermal behavior in magnet quench using the MIITS method. These programs can provide useful quenching parameters (like minimum quench energy and normal zone propagation velocity) for different cases of quench, leading to the previously mentioned objective of magnet design optimization. To test the programs, preliminary cases were run and the data produced was compared and analyzed. The results of these analyses, as well as the program operating methods, are discussed in this project.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Recovery Act - Measurement of Parity Violation in Deep Inelastic Scattering and Studies of the Nucleon Spin Structure at JLab 6 and 11 GeV

The program proposed contains two ingredients which aim to address aspects of two of the three research frontiers of nuclear science as identified in the 2007 NSAC Long Range Plan. The first topic, a test of the current Standard Model, is an ongoing project focusing on measurements of the parity-violating asymmetry in ~e-2H deep inelastic scattering (PVDIS). The PVDIS measurement is complementary to other completed or ongoing low- to medium-energy tests of the Standard Model. As the first, exploratory, step, an experiment using a 6 GeV electron beam will be carried out from October to December 2009 at the Thomas Jefferson National Accelerator Facility (JLab). Meanwhile, a program using the upgraded JLab 11 GeV beam is being planned. The PVDIS program as a whole will provide the first precision data on the axial quark neutral-weak coupling constants. This will either put the current Standard Model to a test that has never been done before, or reveal information on where to look for New Physics beyond the current Standard Model. The PVDIS program will also provide results on hadronic physics effects such as charge symmetry violation. The second part of the proposed program uses spin observables to address the research frontier concerning QCD and structure of the nucleon. An experiment using the JLab 6 GeV beam in 2001 showed that, contrary to predictions from perturbative quantum chromodynamics (pQCD), while the valence up quark’s spin is parallel to the nucleon’s spin, the valence down quark’s spin is not. In order to test the limit of QCD in describing the nucleon spin structure to a region beyond the 6 GeV kinematics, this measurement will be extended to a more energetic, “deeper” valence quark region using the upgraded JLab 11 GeV beam with a polarized 3 He target. Although the two topics of the proposed program appear to focus on different physics, for the upgraded JLab 11 GeV beam, both will utilize a new, yet-to-be-built large acceptance spectrometer, called “SoLID”. Once built, SoLID can be used for other topics such as using PVDIS on a proton target to measure the valence quark distributions, using PVDIS on a polarized target to measure new electro-weak interference structure functions of the nucleon, and semi-inclusive deep inelastic scattering experiments. SoLID will greatly enhance the exploration potential of JLab. The proposed program will first focus on completion of the 6 GeV PVDIS experiment, its data analysis and publishing physics results. This 6 GeV experiment will explore whether precision PVDIS measurements are feasible, will set limits on the hadronic physics effects, and will improve our knowledge on the quark neutral weak couplings, optimally by a factor of six. Starting early 2010, efforts will be spent on preparation for the 11 GeV program, focusing on simulations of SoLID, optimization and construction of the SoLID detector package, and studies of the polarized 3 He target and its improvements. Funding support for the whole program is requested here.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem

Abstract Solving multi‐objective combinatorial optimization problems can quickly become computationally challenging when applied to large networks generated from big data. Present‐day first‐rate Mixed Integer Programming (MIP) solvers have parallelism built‐in to take advantage of multicore architectures; but specialized network optimization algorithms that can often solve graph problems more efficiently than a general MIP solver are typically programmed serially. Thus, these network algorithm implementations do not take full advantage of modern multicore computer capabilities. Many parallel computing languages include a fork/join framework into their concurrency packages that allow for simple conversion of recursive serial algorithms to operate in parallel without having to use complicated low‐level message‐passing interfaces. Fork/join is particularly well suited to divide and conquer algorithms such as Non‐Inferior Set Estimation (NISE) method for computing the supported Pareto front of a multi‐objective optimization problem. This work develops a simple general parallel NISE (pNISE) implementation using the Java 7 concurrency tools, and then offers results from a specific implementation of solving a bi‐objective shortest path problem. The results indicate that this method is capable of sizeable speed‐ups on both small‐scale personal computers as well as large‐scale shared memory supercomputers. Finally, other network problems suitable to this method are discussed, including multi‐objective variants of the minimum spanning tree problem, transportation problem, assignment problem, maximum flow problem, and the minimum‐cost flow problem. Copyright © 2015 John Wiley & Sons, Ltd.

Medrano, Fernando Antonio↗
DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗