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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Combustion plasma electrical conductivity model development for oxy-fuel MHD applications

Oxy-combustion can enable efficient CO2 capture from fossil fuel power plants in combined cycle systems; however, air separation is expensive. In order to directly utilize the high gas temperatures associated with oxy-combustion and offset any oxygen separation penalties, a magnetohydrodynamic (MHD) generator has been proposed as a plant topping cycle. Accurately predicting the electrical conductivity of combustion products with the addition of potassium “seed” compound is necessary to evaluate the performance of this modern approach to MHD. In order to make these predictions, improved species collisional cross-section data (momentum transfer cross-section, MTCS) are needed at relevant conditions. A gas electrical conductivity model for use in open-cycle MHD power generation applications is presented, which makes use of updated MTCS data not represented in previous legacy publications. Based on the results of a detailed review and analysis of currently available MTCS data relevant to open-cycle MHD combustion systems, recommendations have been provided for relevant species and a gas temperature range of 1500-3500 K (~0.13-0.3 eV electron energy). Model predictions utilizing updated MTCS data are validated against limited experimental data found in literature for oxy-combustion with potassium seed compound. Model results are presented from a parametric study, which show the effect of combustion conditions and seeding on ionization processes and gas electrical conductivity, highlighting differences between modern oxy-combustion MHD systems and legacy approaches implementing air-fired combustion and high levels of preheat.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using neural networks. These neural networks can be exactly transformed into mixed integer linear programs and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the neural network. Accordingly, this paper develops a technique for training an "optimally compact'' neural network, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We demonstrate the use of this neural network as an approximator of the nonlinear power flow equations by embedding it in the AC unit commitment problem, transforming the problem from a mixed integer nonlinear program into a more manageable mixed integer linear program. We use the 14-, 57-, and 89-bus networks as test cases and compare the AC-feasibility of commitment decisions resulting from the neural network, DC, and linearized power flow approximations. Our results show that the neural network model outperforms both the DC and linearized power flow approximations when embedded in the unit commitment problem. The neural network formulation most often selects a feasible unit commitment schedule, and furthermore, it only s

AC power flow↗
DOE OSTI2017

A broadly-applicable unified closure relation for Taylor bubble rise velocity in pipes with stagnant liquid

Taylor bubble velocity in slug flow is a closure relation which significantly affects the prediction of liquid holdup (or void fraction) and pressure gradient in mechanistic models of slug flow for oil and gas pipe applications. In this work, we use a validated Computational Fluid Dynamics (CFD) approach to simulate the motion of Taylor bubbles in pipes; the interface is tracked with a Level-Set method implemented in a commercial code. A large numerical database is generated covering the most ample range of fluid properties and pipe inclination angles explored to date (Eo ∊ [10, 700],Mo ∊ [1 x 10 -6 , 5 x 10 3 , and θ ∊ [0°, 90°]). A unified Taylor bubble rise velocity correlation is extracted from that database. The new correlation predicts the numerical database with 8.6% absolute average relative error and a coefficient of determination R 2 = 0.97 and other available experimental data with 13.0% absolute average relative error and R 2 = 0.84 outperforming existing correlations and models.

Mechanics↗
DOE OSTI2018

A scalable geometric multigrid solver for nonsymmetric elliptic systems with application to variable-density flows

A geometric multigrid algorithm is introduced for solving nonsymmetric linear systems resulting from the discretization of the variable density Navier–Stokes equations on nonuniform structured rectilinear grids and high-Reynolds number flows. The restriction operation is defined such that the resulting system on the coarser grids is symmetric, thereby allowing for the use of efficient smoother algorithms. To achieve an optimal rate of convergence, the sequence of interpolation and restriction operations are determined through a dynamic procedure. A parallel partitioning strategy is introduced to minimize communication while maintaining the load balance between all processors. To test the proposed algorithm, we consider two cases: 1) homogeneous isotropic turbulence discretized on uniform grids and 2) turbulent duct flow discretized on stretched grids. Testing the algorithm on systems with up to a billion unknowns shows that the cost varies linearly with the number of unknowns. This O (N) behavior confirms the robustness of the proposed multigrid method regarding ill-conditioning of large systems characteristic of multiscale high-Reynolds number turbulent flows. The robustness of our method to density variations is established by considering cases where density varies sharply in space by a factor of up to 104, showing its applicability to two-phase flow problems. Strong and weak scalability studies are carried out, employing up to 30,000 processors, to examine the parallel performance of our implementation. Excellent scalability of our solver is shown for a granularity as low as 104 to 105 unknowns per processor. At its tested peak throughput, it solves approximately 4 billion unknowns per second employing over 16,000 processors with a parallel efficiency higher than 50%.

Computer Science↗
DOE OSTI2017

Eigendecomposition model of resistance temperature detector with applications to S-CO 2 cycle sensing

Super-critical carbon dioxide (S-CO2) is a promising thermodynamic cycle for advanced nuclear reactors and solar energy conversion applications. Dynamic control of the proposed recompression S-CO2 cycle is accomplished with input from resistance temperature detector (RTD) measurements of the process fluid. One of the challenges in practical implementation of S-CO2 cycle is high corrosion rate of component and sensor materials. In this paper, we develop a mathematical model of RTD sensing using eigendecomposition model of radial heat transfer in a layered long cylinder. We show that the value of RTD time constant primarily depends on the rate of heat transfer from the fluid to the outer wall of RTD. We also show that for typical material properties, RTD time constant can be calculated as the sum of reciprocal eigen-values of the heat transfer matrix. Using the computational model and a set of RTD and CO2 fluid thermophysical parameter values, we calculate the value of time constant of thermowell-mounted RTD sensor at the hot side of the precooler in the S-CO2 cycle. The eigendecomposition model of RTD will be used in future studies to model sensor degradation and its impact on control of S-CO2. (C) 2016 Elsevier B.V. All rights reserved.

finite element↗
DOE OSTI2017

Taking Lessons Learned from a Proxy Application to a Full Application for SNAP and PARTISN

SNAP is a proxy application which simulates the computational motion of a neutral particle transport code, PARTISN. Here in this work, we have adapted parts of SNAP separately; we have re-implemented the iterative shell of SNAP in the task-model runtime Legion, showing an improvement to the original schedule, and we have created multiple Kokkos implementations of the computational kernel of SNAP, displaying similar performance to the native Fortran. We then translate our Kokkos experiments in SNAP to PARTISN, necessitating engineering development, regression testing, and further thought.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Postsynthetic Tuning of Metal–Organic Frameworks for Targeted Applications

Metal–organic frameworks (MOFs) are periodic, hybrid, atomically well-defined porous materials that typically form by self-assembly and consist of inorganic nodes (metal ions or clusters) and multitopic organic linkers. MOFs as a whole offer many intriguing properties, including ultrahigh porosity, tunable chemical functionality, and low density. These properties point to numerous potential applications, including gas storage, chemical separations, catalysis, light harvesting, and chemical sensing, to name a few. Reticular chemistry, or the linking of molecular building blocks into predetermined network structures, has been employed to synthesize thousands of MOFs. Given the vast library of candidate nodes and linkers, the number of potentially synthetically accessible MOFs is enormous. Nevertheless, a powerful complementary approach to obtain specific structures with desired chemical functionality is to modify known MOFs after synthesis. This approach is particularly useful when incorporation of particular chemical functionalities via direct synthesis is challenging or impossible. Here, the challenges may stem from limited stability or solubility of precursors, unwanted secondary reactivity of precursors, or incompatibility of functional groups with the conditions needed for direct synthesis. MOFs can be postsynthetically modified by replacing the metal nodes and/or organic linkers or via functionalization of the metal nodes and/or organic linkers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bimetallic Metal–Organic Framework Fe/Co-MIL-88(NH 2 ) Exhibiting High Peroxidase-like Activity and Its Application in Detection of Extracellular Vesicles

Metal–organic frameworks (MOFs) have many attractive features, including tunable composition, rigid structure, controllable pore size, and large specific surface area, and thus are highly applicable in molecular analysis. Depending on the MOF structure, a high number of un-saturated metal sites can be exposed to catalyze chemical reactions. In the present work, we report that by using both Co(II) and Fe(III) to prepare the MIL-88(NH 2 ) MOF, we can produce the bimetallic MOF that can catalyze the conversion of 3,3', 5,5"-tetramethylbenzidine (TMB) to a color product through reaction with H 2 O 2 at a higher reaction rate than the monometallic Fe-MIL-88(NH 2 ). The Michaelis constants (K m ) of the catalytic reaction for TMB and H 2 O 2 are 3-5 times smaller, and the catalytic constants (k cat ) are 5-10 times higher than those of the horse-radish peroxidase (HRP), supporting ultrahigh peroxidase-like activity. These values are also much more superior to those of the HRP-mimicking MOFs reported previously. Interestingly, the bimetallic MOF can be coupled with glucose oxidase (GOx) to trigger the cascade enzymatic reaction for highly sensitive detection of extracellular vesicles (EVs), a family of important biomarkers. Through conjugation to the aptamer that recognizes the marker protein on EV surface, the MOF can help isolate the EVs from biological matrices, which are subsequently labeled by GOx via antibody recognition. The cascade enzymatic reaction between MOF and GOx enables detection of EVs at a concentration as low as 7.8 × 10 4 particles/ml. Further, the assay can be applied to monitor EV secretion by cultured cells, and also can successfully detect the different EV quantities in the sera samples collected from cancer patients and healthy controls. Overall, we prove that the bimetallic Fe/Co-MIL-88(NH 2 ) MOF, with its high peroxidase-activity and high biocompatibility, is a valuable tool deployable in clinical assays to facility disease diagnosis and prognosis.

36 MATERIALS SCIENCE↗