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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 OSTI2018

In vivo biosensors: mechanisms, development, and applications

Abstract In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

Shi, Shuobo↗
DOE OSTI2016

State of practice and emerging application of analytical techniques of nuclear forensic analysis: highlights from the 4th Collaborative Materials Exercise of the Nuclear Forensics International Technical Working Group (ITWG)

Founded in 1996 upon the initiative of the “Group of 8” governments (G8), the Nuclear Forensics International Technical Working Group (ITWG) is an ad hoc organization of official nuclear forensics practitioners (scientists, law enforcement, and regulators) that can be called upon to provide technical assistance to the global community in the event of a seizure of nuclear or radiological materials. The ITWG is supported by and is affiliated with roughly 40 countries and international partner organizations including the International Atomic Energy Agency (IAEA), EURATOM, INTERPOL, EUROPOL, and the United Nations Interregional Crime and Justice Research Institute (UNICRI). Besides providing a network of nuclear forensics laboratories that are able to assist law enforcement during a nuclear smuggling event, the ITWG is also committed to the advancement of the science of nuclear forensic analysis, largely through participation in periodic table top and Collaborative Materials Exercises (CMXs). Exercise scenarios use “real world” samples with realistic forensics investigation time constraints and reporting requirements. These exercises are designed to promote best practices in the field and test, evaluate, and improve new technical capabilities, methods and techniques in order to advance the science of nuclear forensics. The ITWG recently completed its fourth CMX in the 20 year history of the organization. This was also the largest materials exercise to date, with participating laboratories from 16 countries or organizations. Three samples of low enriched uranium were shipped to these laboratories as part of an illicit trafficking scenario, for which each laboratory was asked to conduct nuclear forensic analyses in support of a fictitious criminal investigation. An objective review of the State Of Practice and Art of international nuclear forensic analysis based upon the outcome of this most recent exercise is provided.

ITWG CMX-4↗
DOE OSTI2018

Experimental and kinetic analysis for particle scale modeling of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier during reduction with H 2 in chemical looping combustion applications [Experimental and kinetic analysis for particle scale modeling of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier during reduction with CO in chemical looping combustion applications]

A kinetic analysis of the H 2 reduction of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier in gas phase fueled Chemical Looping Combustion of synthesis gas was utilized to derive particle scale representation. An experimentally driven study was carried out to provide an array of operational data sets for kinetic modelling approaches. The impact of key operational variables on the kinetics of the novel oxygen carrier were examined, with emphasis on the application of reliable phenomena driven particle scale models to describe the reduction behavior. Due to the novel nature of the material, a series of experimental studies were carried out to provide a fundamental understanding of how the material changed as oxygen was depleted from the structure due to reduction with H 2 . This include quantification of the complex mixed metal oxide phase and changes due to lattice oxygen depletion. It was found that H 2 reduction occurs in a multistep process where CuFeAlO 4 → Cu 0+ + FeAl 2 O 4 → Cu 0+ + Fe 0+ + Al 2 O 3 as oxygen is depleted from the structure. This multistep process was successfully emulated through the use of a two interface Grainy pellet model in which reaction (kinetic) control was the main rate limiting step. This is validated through the examination of other potential rate limiting resistances. In conclusion, the model emulates changes in key operation variables with good accuracy.

42 ENGINEERING↗
DOE OSTI2022

Application of a Novel Heat Pump Model for Estimating Economic Viability and Barriers of Heat Pumps in Dairy Applications in the United States

Heat pumps represent an important opportunity for energy savings and decarbonization. This work investigates the techno-economic performance of high-temperature heat pumps (HTHPs) for use in the U.S. dairy industry. The studied heat pump performs a 50 °C temperature lift on a waste heat stream of cleaning water and applies the upgraded heat stream to a fluid milk pasteurization process. This work involved the creation of a HTHP model that estimated the coefficient of performance (COP), internal rate of return (IRR), net present value (NPV), and payback period (PBP), and emissions saved for a heat pump replacing a natural gas boiler. Capital costs, operations, and maintenance (O&M) cost, heat pump lifetime, electricity prices, natural gas prices, and a cost of carbon were varied to perform a parametric study on the factors affecting the break-even price of HTHPs. The results show that HTHP economics are highly sensitive to COP and energy price environment, and less sensitive to capital and O&M cost variance, leading to a large scatter of positive and negative NPVs based on U.S. location. PBPs demonstrate a defined threshold, based on energy price environment, below which favorable two-to-three-year PBPs predominate. This work is focused on the U.S. dairy industry, but international application in relation to fossil vs. electricity price regimes. Heat pumps have seen wider adoption in regions with a high ratio of fossil energy to electricity prices ($/MMBTU vs. $/kWh). The U.S. has plentiful natural gas resulting in lower fossil energy prices which has reduced heat pump adoption. This paper identifies potential first mover industries for HTHP adoption and their associated price regimes even in regions with lower ratios of fossil energy to electricity prices that exist many places globally.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
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↗