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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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Learn about the types of home ventilation and how ventilation can help you avoid moisture problems. This fact sheet from Energy Saver includes information why it's important to ventilate and how you can use ventilation strategies to control moisture in your home for comfort and energy savings.
This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.
This letter formulates and solves a periodic trajectory optimization problem for a tethered underwater kite. The goal is to maximize the average mechanical power harvested by the kite. The type of kite considered in this letter extracts electricity from ocean currents by moving cross-current as its reel away from its base station, and consumes electricity to reel back. The problem of optimizing this kite’s trajectory is challenging due to the high dimensionality and nonlinearity of its dynamics. To tackle this challenge, the literature often separates the problem into two subproblems focusing on optimizing the cross-current and the reel-in/reel-out components of the trajectory, respectively, which may be sub-optimal. In contrast, this letter solves for the combined cross-current and reel-in/reel-out trajectory by linearizing the dynamics of the kite around a zero-power reference equilibrium trajectory in spherical coordinates. This allows the trajectory optimization problem to be solved analytically for simple sinusoidal input perturbations from equilibrium. Here we use linear quadratic regulation to enable the nonlinear kite model to track the optimized trajectory. The result is a computationally efficient approach that achieves an attractive Loyd factor of 19.9%, while providing important insights into the nature of the optimal trajectory.
Density matrix electronic structure theory is used in many quantum chemistry methods to “alleviate” the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree–Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave’s new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. Finally, we also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.
We present an implementation of the transient method of characteristics (MOC) with isotropic time derivatives, accelerated by diffusion synthetic acceleration (DSA). The fully implicit frequency transform method is used to solve the transient problem with analytic precursor integration. The code works on meshes composed of almost any of the commonly used non-curvilinear finite element types, and can handle the deformation of geometry in time-dependent transport calculations. We present results of a continuous Fourier analysis for the transient multigroup DSA problem, and representative benchmarking results are presented for the C5G7-TD benchmark in 2D showing reasonable performance and agreement compared to other codes. (authors)
We looked to build a new type of 100 kV pulse generator that we hope will avoid many of the problems seen with other commercially available pulse generators. We looked to exploit recent improvements in both lasers and solid-state pulse charge units along with an alternative triggering layout to build something new. For the pulse charging unit, we collaborated with Texas Tech to leverage their knowledge and expertise in pulse transformers and solid-state switching. The final result of the LDRD looks promising. Texas Tech was able to build a pulse charge unit that meets our specifications regarding output, and we were able to integrate it with a switch and a laser to create a device that meets the goals of the LDRD. There is still much work to do to advance the design further and improve reliability, but the proto-type design satisfies the goals we set for this LDRD.
Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.
Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.
Highlights: • The kinetics of water release from SAPs were studied by neutron tomography. • The time window for internal curing was linked to the water release from SAPs. • The water release links to the SAP effectiveness to mitigate autogenous shrinkage. • One SAP type was effective while another prematurely released its stored water. Superabsorbent polymers (SAPs) are used to counteract self-desiccation in order to mitigate autogenous shrinkage, a problem in cementitious systems with a low water-to-cement ratio. The release kinetics during internal curing are of importance as not all SAP types are able to efficiently mitigate autogenous shrinkage. In this study, neutron tomography is used to study and visualize the water release kinetics over time. Per-voxel analysis of the time-attenuation curve was performed using piecewise-constant functions. Two different SAP types were studied, one being able to mitigate autogenous shrinkage and one quickly releasing its stored water after final setting. The tomography results correspond to autogenous shrinkage measurements and nuclear magnetic resonance tests. The visualization provides information on the time of water release by the SAPs after setting and the time window of internal curing. This opens additional insights towards the application of SAPs in the construction sector and provides information on the mechanism of internal curing.
Abstract High-coverage sequencing allows the study of variants occurring at low frequencies within samples, but is susceptible to false-positives caused by sequencing error. Ion Torrent has a very low single nucleotide variant (SNV) error rate and has been employed for the majority of human papillomavirus (HPV) whole genome sequences. However, benchmarking of intrahost SNVs (iSNVs) has been challenging, partly due to limitations imposed by the HPV life cycle. We address this problem by deep sequencing three replicates for each of 31 samples of HPV type 18 (HPV18). Errors, defined as iSNVs observed in only one of three replicates, are dominated by C→T (G→A) changes, independently of trinucleotide context. True iSNVs, defined as those observed in all three replicates, instead show a more diverse SNV type distribution, with particularly elevated C→T rates in CCG context (CCG→CTG; CGG→CAG) and C→A rates in ACG context (ACG→AAG; CGT→CTT). Characterization of true iSNVs allowed us to develop two methods for detecting true variants: (1) VCFgenie, a dynamic binomial filtering tool which uses each variant’s allele count and coverage instead of fixed frequency cut-offs; and (2) a machine learning binary classifier which trains eXtreme Gradient Boosting models on variant features such as quality and trinucleotide context. Each approach outperforms fixed-cut-off filtering of iSNVs, and performance is enhanced when both are used together. Our results provide improved methods for identifying true iSNVs in within-host applications across sequencing platforms, specifically using HPV18 as a case study.
Interdigital capacitor (IDC) sensors are attractive in-situ sensors that can be used to measure permittivity variations in materials. Such sensors could be powerful enablers for cable insulation aging detection. Since insulation aging is correlated with a change in its material permittivity an IDC can measure that change via its measured capacitance. A new thin, flexible fabric-based IDC sensor is introduced for application on conformal curved surfaces such as an electrical cable surface. A multielectrode IDC and its backplane were fabricated using conductive copper polyester taffeta fabrics that were adhesively bonded to nonconductive polyester fabric. The sensor because of its soft flexible structure eliminates the problem of sensor conductor damage which is common for printed circuit board (PCB) type sensors when conformed to the surface of a cable. It also significantly reduces the airgap between the electrodes and the surface that they must contact in order to measure the capacitance. Airgap reduction and consistency are important in order to accurately predict permittivity variations in cable insulation material due to aging. Both PCB-based sensors and the new fabric-based sensors were built and tested on unaged cables and cables that underwent accelerated aging. Results demonstrate the proposed advantages of the novel fabric-based IDC sensor.
The VERAOneWay (VOW) package provides one-way coupling between two Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes: the Virtual Environment for Reactor Applications (VERA) and BISON. This package was initially designed and tested on multicycle pressurized water reactor (PWR) problems. Recent work has improved VOW and extended its capability to other problems. VOW was enhanced to simulate fuel performance for reactor transients. Additional fuel types and geometries were added to cover boiling water reactor (BWR) fuels. The BISON version and templates were updated to the most recent version and best practices. The code was refactored to make it easier to add features and to extend VOW to other codes.
Critical Problem: How to organize user-specified patterns of particles of any material type over a macroscale area and volume. Proposed Solution: Ultrasound directed self-assembly (DSA).
Nuclear fuels using alloys of uranium, or metallic fuels, have many beneficial properties. The classical metallic fuel design uses a loose fitting cylindrical “slug” of fuel placed inside stainless-steel cladding tubes where the gap is filled with sodium. This sodium bond is liquid at operating temperature and conducts heat from the slug to the cladding, especially in early life before fuel swells into contact with the cladding. Despite the benefits of sodium bonding, there is a desire to develop metallic fuel technologies without it chiefly to reduce chemical reaction hazards in spent fuel storage from sodium fast reactors operating on once-through fuel cycles. Elimination of the sodium bond may also help unlock potential benefits for fuel fabrication, reactor neutronics, and compatibility with other types of reactors. Creating a sodium-free metallic fuel revolves around the problem of manufacturing fuel slug geometries which are in close contact with the cladding at beginning of life to facilitate heat transport while alleviating fuel-cladding chemical interactions (FCCI) at this interface and providing enough free volume to accommodate fuel swelling. Accelerating development and qualification of this fuel system will require careful selection of design and manufacturing options. To this end, a design trade-off study was performed to evaluate candidate options. Several design and manufacturing options were assessed, weighted, scored, and ranked with respect to fabrication, normal reactor operation, off-normal scenarios, and back-end considerations. This effort was performed both for “baseline” needs, which represented a once-through fuel cycle at temperatures and burnups known to be viable for sodium-bonded metallic fuel, and for “enhanced” needs to represent opportunities for closed fuel cycles and/or more aggressive temperatures/burnups. The outcomes of this study prioritized a baseline technology using U-Zr alloy with additives to mitigate FCCI, produced in annular slug geometry by continuous casting, clad in austenitic stainless-steel alloy, and followed by a final step to swage the cladding down to close the gap. This study prioritized an enhanced fuel technology using U-Mo alloy, also produced by continuous casting into an annular geometry, followed by coating/plating with an FCCI barrier on the slug, again with a final step to swage the cladding diameter down using oxide dispersion strengthened steel. It was noted that development of the enhanced fuel technology would entail more risk, thus U-Zr alloy was put forth as a backup to U-Mo if challenges are encountered with FCCI barriers, and advanced ferritic/martensitic steels are put forth as a backup to oxide dispersion strengthened steels if swaging and welding are found unworkable.
The use of gradient-based data-driven models to solve a range of real-world remote sensing problems can in practice be limited by the uniformity of available data. Use of data from disparate sensor types, resolutions, and qualities typically requires compromises based on assumptions that are made prior to model training and may not necessarily be optimal given over-arching objectives. For example, while deep neural networks (NNs) are state-of-the-art in a variety of target detection problems, training them typically requires either limiting the training data to a subset over which uniformity can be enforced or training independent models which subsequently require additional score fusion. The method we introduce here seeks to leverage the benefits of both approaches by allowing correlated inputs from different data sources to co-influence preferred model solutions, while maintaining flexibility over missing and mismatching data. In this work we propose a new data fusion technique for gradient updated models based on entropy minimization and experimentally validate it on a hyperspectral target detection dataset. We demonstrate superior performance compared to currently available techniques using a range of realistic data scenarios, where available data has limited spacial overlap and resolution.
The interaction of plasma with materials is critical for the fundamental understanding of non-equilibrium processes and their wide application. Recent achievements in fusion energy research emphasize the importance of this problem. Because modelling and predicting plasma–material interactions (PMI) require considering a tremendous number of single plasma particle–surface interaction events, this type of modelling was, until recently, possible only within a binary collision approximation (BCA). The BCA approach considers materials as uniform temperature-insensitive media. The research presented here addresses the PMI problem within an atomistic-based approach for the first time at a statistically significant level. Approximately 10 5 molecular dynamics trajectories were generated for 100 eV deuterium ions interacting with a tungsten surface. The research demonstrated the critical importance of incorporating the discrete lattice structure of matter into the model. Deuterium penetration depth and fraction of backscattered deuterium ions strongly depend on the surface orientation, the impact incident ion directions, and the ions’ initial positions. On average, the BCA-based calculations underestimate the penetration depth by a factor of two or more, and the fraction of backscattered atoms is overestimated by a factor of two or more. As a result, increasing the material temperature from 500 to 3000 K reduces penetration depth by about 30% and increases the fraction of backscattered atoms by 250%.
Consider the class of zero-mean functions with fixed L ∞ and L 1 norms and exactly N ϵ N nodal points. Which functions f minimize W p (f + ,f – ), the Wasserstein distance between the measures whose densities are the positive and negative parts? We provide a complete solution to this minimization problem on the line and the circle, which provides sharp constants for previously proven “uncertainty principle”-type inequalities, i.e., lower bounds on N • W p (f + ,f – ). We further show that, while such inequalities hold in many metric measure spaces, they are no longer sharp when the non-branching assumption is violated; indeed, for metric star-graphs, the optimal lower bound on W p (f + ,f – ) is not inversely proportional to the size of the nodal set, N. Here, based on similar reductions, we make connections between the analogous problem of minimizing W p (f + ,f – ) for f defined on Ω C R d with an equivalent optimal domain partition problem.
While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. Here, the results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem.