SEARCH · Engineering Papers
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.
This technical presentation provides an update on the major trends that occurred in the solar industry in the first three quarters of 2019. Major topics of focus include costs and deployment, the global and U.S. supply and demand of PV, module and system price, investment trends and business models, and updates on U.S. government programs supporting the solar industry.
Theoretical research has long played an essential role in interpreting data from high-energy particle colliders and motivating new accelerators to advance the energy and precision frontiers. Collider phenomenology is an essential interface between theoretical models and experimental observations, since theoretical studies inspire experimental analyses while experimental results sharpen theoretical ideas. This report -- from the Snowmass 2021 Theory Frontier topical group for Collider Phenomenology (TF07) -- showcases the dynamism, engagement, and motivations of collider phenomenologists by exposing selected exciting new directions and establishing key connections between cutting-edge theoretical advances and current and future experimental opportunities. By investing in collider phenomenology, the high-energy physics community can help ensure that theoretical advances are translated into concrete tools that enable and enhance current and future experiments, and in turn, experimental results feed into a more complete theoretical understanding and motivate new questions and explorations.
The 7 th workshop of the NNSA-IAEC collaboration was conducted on November 8-10 2022. The workshop was held at the geologic survey of Israel, Jerusalem. The IAEC hosted the workshop. The main topic of the workshop evolved around the results of the characterization borehole drilled in the Yamin plain during 2022 and associated experimental work and modeling results. The new data will be integrated for the applicability analysis of intermediate depth borehole (IDB) disposal for spent fuel in the Yamin plain. The first day of the workshop (November 8 th ) was dedicated to a geological field trip in the Judea desert depicting a similar cross section to that found in the Yamin plain. During the 9 th and 10 th of November the characterization BH results were presented, results of new work on the Miocene Hazeva fm. were presented, and this year’s outcome of the different collaboration groups were given.
Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 Phase 2 to undertake cooperative research in the key topic of blade erosion. The purpose of Task 46 Erosion Phase 2 is to further improve understanding of erosion driving factors, develop datasets and model tools to enhance prediction of leading-edge erosion likelihood, identify damage at the earliest possible stage and advance potential solutions. The scope of work covers several technical areas, reflecting the multidisciplinary nature of the challenge. Participants in the task are given in Table 1.
Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.
Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.
Electric vehicle (EV) adoption will change the composition of EV charging load to higher-power charging as more medium- (MD) and heavy-duty (HD) applications are electrified, and as all vocations, including light-duty (LD) vehicles, are capable of faster charging. These shifts provide the opportunity for high-power charging (HPC) and facility equipment to evolve and improve efficiency, cost, and space. High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP) project designs and develops a high-power, interoperable charging experimental platform to research, develop, and demonstrate the integration approaches and technology solutions. The project addresses (1) interconnection and management of a grid-tied inverter; (2) development of a DC distribution system that is responsible for system energy management, interoperability, and DC protection; (3) modular DC/DC conversion for vehicle charging; (4) EV charging interface and DC/DC integration; and (5) smart charge control and vehicle-to-edge (vehicle-to-building [V2B], vehicle-to-everything [V2X]) capability. This summary presentation is the first technical progress output of the project. It provides insight for the first deep-dive technical meeting outputs in terms of research presentations and also includes summary of the discussions occurred in the follow-up breakout sessions. The summary presentation covers three technical areas: (1) HPC: State of the art power architectures and the design of the power electronics, (2) Modeling, energy management, and power control in the HPC station, and (3) Codes and standards work that are in line with the previous two topic areas.
BISON is a modern nuclear fuel performance code that has been under development at Idaho National Laboratory (INL) since 2009. It is a finite element-based code capable of solving the energy, mechanics, and species diffusion equations in a fully-coupled sense. BISON also utilizes thermal and mechanical contact algorithms and variety of nonlinear material models. BISON is applicable to steady and transient fuel behavior and supports the use of one-, two-, and three-dimensional meshes. Although thus far used primarily to simulate light water reactor fuel, BISON is designed to be a multi-fuels code. As such, BISON has been used to analyze TRISO-coated particle fuel and metal fuel in rod and plate form, design and interpret fuel irradiation experiments and investigate novel fuel concepts. This publication provides a preliminary list of metal fuel-related material models recently implemented in BISON. Some preliminary results from a simulation of the EBR-II X447 experiment are shown, which begins to demonstrate how all these models work together. A follow-up publication will be presented with more detail at the ANS meeting in New Orleans during the embedded topical meeting titled 'Nuclear Fuels and Structural Materials for the Next Generation Nuclear Reactors'. (authors)
A proper description of inclusive reactions is expressed with density matrices. Quantum tomography reconstructs density matrices from experimental observables. We review recent work that applies quantum tomography to practical experimental data analysis. Almost all field-theoretic formalism and modeling used in a traditional approach is circumvented with great efficiency. Tomographically-determined density matrices can express information about quantum systems which cannot in principle be expressed with distributions defined by classical probability. Topics such as entanglement and von Neumann entropy can be accessed using the same natural language where they are defined. A deep relation exists between separability, as defined in quantum information science, and factorization, as defined in high energy physics. Factorization acquires a non-perturbative definition when expressed in terms of a conditional form of separability. An example illustrates how to go from data for momentum 4-vectors to a density matrix while bypassing almost all the formalism of the Standard Model.
The first 4D (axisymmetric) high-order continuum gyrokinetic transport simulations that span the magnetic separatrix of a tokamak are presented. The modeling is performed with the COGENT code, which is distinguished by fourth-order finite-volume discretization combined with mapped multiblock grid technology to handle the strong anisotropy of plasma transport and the complex X-point divertor geometry with high accuracy. The calculations take into account the effects of fully nonlinear Fokker-Plank collisions, electrostatic potential variations, and anomalous radial transport. Topics discussed include: (a) ion orbit loss and the associated toroidal rotation and (b) edge plasma relaxation in the presence of anomalous radial transport.
Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.
The two primary purposes of LANL’s Computational Physics Student Summer Workshop are (1) To educate graduate and exceptional undergraduate students in the challenges and applications of computational physics of interest to LANL, and (2) Entice their interest toward those challenges. Computational physics is emerging as a discipline in its own right, combining expertise in mathematics, physics, and computer science. The mathematical aspects focus on numerical methods for solving equations on the computer as well as developing test problems with analytical solutions. The physics aspects are very broad, ranging from low-temperature material modeling to extremely high temperature plasma physics, radiation transport and neutron transport. The computer science issues are concerned with matching numerical algorithms to emerging architectures and maintaining the quality of extremely large codes built to achieve multi-physics calculations. Although graduate programs associated with computational physics are emerging, it is apparent that the pool of U.S. citizens in this multidisciplinary field is relatively small and is typically not focused on the aspects that are of primary interest to LANL. Furthermore, more structured foundations for LANL interaction with universities in computational physics is needed; currently interactions rely almost solely on individuals’ personalities and personal contacts. Thus a tertiary purpose of the Summer Workshop is to build an educational network of LANL researchers, university professors, and emerging students to advance the field and LANL’s involvement in it. This was the second year for the Summer Workshop. Like the previous year, the workshop’s goals were achieved by bringing into LANL a select group of students recruited from across the United States and immersing them for ten weeks in lectures and interesting research projects. The lectures provided an overview of the computational physics topics of interest this year along with some detailed instruction while the projects gave the students a positive experience accomplishing technical goals. Each team consisted of two students working under one or more mentors from LANL on specific research projects associated with predefined topics. This year, the topics were on verification of simulations in plasma and radiation-hydrodynamics codes, hypervelocity material deformation, plasma physics, graphical processor unit (“GPU”) programming, multi-material mixing, turbulence modeling, computational material science, and electromagnetic pulse simulations. The students’ growth was furthered by their participation on teams where their teammates were sometimes of different academic rank. It also developed them by requiring them to produce written and oral reports that they presented to peers, mentors, and management.
Several topics in the theory of generalized parton distributions (GPDs) are reviewed. First, we give a brief overview of the basics of the theory of generalized parton distributions and their relationship with simpler phenomenological functions, viz. form factors, parton densities and distribution amplitudes. Then, we discuss recent developments in building models for GPDs that are based on the formalism of double distributions (DDs). A special attention is given to a careful analysis of the singularity structure of DDs. The DD formalism is applied to construction of a model GPDs with a singular Regge behavior. Within the developed DD-based approach, we discuss the structure of GPD sum rules. It is shown that separation of DDs into the so-called “plus” part and the D -term part may be treated as a renormalization procedure for the GPD sum rules. This approach is compared with an alternative prescription based on analytic regularization.
The tokamak is a type of toroidal device used to confine a fusion plasma using large magnetic fields. Tokamaks and stellarators the leading devices for confining plasmas for fusion, and the capability to predict performance in these magnetically confined plasmas is essential for developing a sustainable fusion energy source. The magnetic configuration of tokamaks and stellarators does not exist in Nature, yet, the fundamental processes governing transport in fusion plasmas are universal – turbulence and instabilities, driven by inhomogeneity and asymmetry in the plasma, conspire to transport heat and particles across magnetic field lines and can play critical roles in impurity confinement and generation of intrinsic rotation. Turbulence exists in all plasmas, and in neutral fluids as well. The study of turbulence is essential to developing a fundamental understanding of the nature of the fourth state of matter, plasmas. Experimental studies of turbulence in tokamaks date back to early scattering observations from the late 1970s. Since that time, great advances in turbulence diagnostics have been made, all of which have significantly enhanced our knowledge and understanding of turbulence in tokamaks. Through comparisons with advanced gyrokinetic theory and turbulent-transport models a great deal of evidence exists to implicate turbulent-driven transport as an important mechanism determining transport in all channels: heat, particle and momentum However, prediction and control of turbulent-driven transport remains elusive. Key to development of predictive transport models for magnetically confined fusion plasmas is validation of the nonlinear gyrokinetic transport model, which describes transport due to turbulence. Validation of gyrokinetic codes must include detailed and quantitative comparisons with measured turbulence characteristics, in addition to comparisons with inferred transport levels and equilibrium profiles. For this reason, advanced plasma diagnostics for studying core turbulence are needed in order to assess the accuracy of gyrokinetic models for turbulent-driven particle, heat and momentum transport. New core turbulence diagnostics at the world-class tokamaks Alcator C-Mod at MIT and ASDEX Upgrade at the Max Planck Institute for Plasma Physics have been designed, developed, and operated over the course of this project. These new instruments are capable of measuring electron temperature fluctuations and the phase angle between density and temperature fluctuations locally and quantitatively. These new data sets from Alcator C-Mod and ASDEX Upgrade are being used to fill key gaps in our understanding of turbulent transport in tokamaks. In particular, this project has results in new results on the topics of the Transport Shortfall, the role of ETG turbulence in tokamak plasmas, profile stiffness, the LOC/SOC transition, and intrinsic rotation reversals. These data are used in a rigorous process of “Transport model validation”, and this group is a world-leader on using turbulence models to design new hardware and new experiments at tokamaks. A correlation electron cyclotron emission (CECE) diagnostic is an instrument used to measure micro-scale fluctuations (mm-scale, compared to the machine size of meters) of electron temperature in magnetically confined fusion plasmas, such as those in tokamaks and stellarators. These micro-scale fluctuations are associated with drift-wave type turbulence, which leads to enhanced cooling and mixing of particles in fusion plasmas and limits achieving the required temperatures and densities for self-sustained fusion reactions. A CECE system can also be coupled with a reflectometer system that measured micro-scale density fluctuations, and from these simultaneous measurements, one can extract the phase between the density (n) and temperature (T) fluctuations, creating an nT phase diagnostic. Measurements of the fluctuations and the phase angle between them are extremely useful for testing and validating predictive models for the transport of heat and particles in fusion plasmas due to turbulence. Once validated, the models are used to predict performance in ITER and other burning plasmas, such as the MIT ARC design. Most recently, data from the newly developed, so-called “CECE diagnostic” [Cima 1995, White 2008] and “nT phase angle measurements” [Haese 1999, White 2010] ]will be combined with data from density fluctuation diagnostics at ASDEX Upgrade to support a long-term program of physics research in turbulence and transport that will allow for more stringent testing and validation of gyrokinetic turbulent-transport codes. This work directly impacts the development of predictive transport models in the U.S. FES program, such as TGLF, developed by General Atomics, which are used to predict performance in ITER and other burning plasma devices as part of advancing the development of fusion energy sciences.
Power system resilience is an emerging topic and plays an essential role in helping the power industry understand and respond to the increasing threats of extreme weather events. The first step of power system resilience analysis is to introduce metrics to quantify the resilience reasonably. Existing resilience metrics are typically restrained by the limited data for extreme event modeling and fall short in terms of physical interpretation and comparability. This paper develops novel quantitative metrics to evaluate power system resilience in pre- and post-event contexts. The developed metrics illustrate clear physical meanings and can be effectively used to compare resilience across different systems under different extreme events. Moreover, the developed metrics can be applied to both transmission and distribution systems. Simulation on a distribution system is employed to validate the effectiveness of the proposed resilience metrics and resilience evaluation approach.
The “constant velocity piston” problem (Fig. 1), also known as the “piston problem,” is a standard model for a one dimensional, in our case linear, symmetric shock wave moving through an inviscid, perfect gas. The model can be divided into two regions - a perturbed section on the left and an unperturbed section on the right - by a moving shock wave moving left to right. Both the perturbed and unperturbed sections, i.e. the shocked and unshocked regions, respectively, obey the Eulerian conservation equations; however, at the exact location of the shock, there is a mathematical discontinuity not satisfied by the Euler equations. To ensure continuity and conservation of certain quantities when crossing between the unshocked and shocked regions, we evoke a series of equations derived from the Eulerian conservation equations, called the Rankine-Hugoniot equations, or “jump” equations as it is often referred to in the literature on the topic. The classical constant-velocity piston problem assumes the piston features a constant driving velocity (among many other willing suspensions of belief required in the pursuit of a first principles equation model); consequent to this assumption is a constant-velocity shock and a constant-velocity shocked flow state. However, using Lie Group Theory (LGT), also known as symmetry analysis, we can attempt to reinterpret the model with a shock wave of variable velocity in time and space. An extension of the model in this way opens up the possibility for obtaining new analytical solutions to the piston problem for certain shock velocity models. In this report, we use LGT to derive the symmetry determining equations (SDEs), whose solutions are Lie groups, which permit analytical solutions. In the future, we can then use the SDEs to define constraint equations on the shock velocity model and what the successive solutions to the Euler equations might be based off such constraints. This report is structured as follows: Section 2 provides a brief derivation of the Rankine-Hugoniot (“jump”) equations; Section 3 gives an overview of Lie group theory; Section 4 derives the SDEs of the jump equations; Section 5 derives the Euler conservation equations for fluids; and Section 6 presents concluding remarks and opportunities for future studies.
The ongoing push towards enhanced energy efficiency and reduced emissions has necessitated the creation of materials with superior performance, especially under extreme conditions. Modern industries, such as aerospace, energy production, and nuclear power, rely heavily on materials that can withstand elevated temperatures without compromising structural integrity. At these heightened temperatures, materials, even when subjected to mechanical stresses well below their yield strength, may experience slow deformation leading to eventual rupture — a phenomenon known as creep. With the expansive design space that comes with the high entropy concept and their reported excellent high temperature strength, multi-principal element materials (MPEMs) have attracted interest in the scientific community for high-temperature applications. Here, this review offers a comprehensive examination of existing studies on creep in MPEMs, which includes multi-principal element−alloys, −bulk metallic glasses, −ceramics, and −superalloys, comparing published findings on MPEMs with pure elements, traditional alloys, bulk metallic glasses, and superalloys. The sub-topics covered include a comparison among different creep-testing methods, creep mechanisms, creep exponents, creep strain rates, activation volume, and creep-activation energy. Modeling efforts for predicting creep behavior of MPEMs are also reviewed. Methods for improving creep resistance by performing heat treatments and/or modifying microstructures are discussed. Overall, the current state of MPEMs has not yet surpassed the creep performance of commercial alloys. Finally, directions for future efforts are suggested, such as experimenting in various controlled environments, expanding the number of compositions tested, exploring advanced manufacturing techniques, and using machine-learning to predict creep properties based on compositions and microstructures.
Morphological and surface characteristics of gamma-Al 2 O 3 are topics of high relevance in the field of catalysis. Using tomography and high-resolution S/TEM imaging, we have studied the surface characteristics of a model gamma-Al 2 O 3 synthesized in the shape of platelets and macroscopically defined by (110)Al 2 O 3 and (111)Al 2 O 3 surface facets. We show that the dominant (110)Al 2 O 3 surface of the synthesized gamma-Al 2 O 3 is not atomically flat but undergoes a significant reconstruction, forming nanoscale (111)Al 2 O 3 terraces. In addition to high resolution imaging, tomographic analysis was carried out, enabling an examination of the pores/voids, which were found to be mostly enclosed within the bulk and inaccessible to gasses or metals. Tomographic analysis shows that the surfaces of the pores are defined exclusively by (100)Al 2 O 3 and (111)Al 2 O 3 facets. The importance of these findings is discussed in the context of relative surface energies of low index surfaces and ethanol desorption characteristics.