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A special edition of the journal Optical Engineering invited several scientists to participate in the editorial board of this special issue. The eleven papers in this special section cover recent developments in active spectroscopy, including new phenomenology and applications, advances in systems and components, and new methods for data processing and exploitation.
Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.
Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.
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The processing and storage of nuclear fuel is an ever increasingly pressing issue. Around 70,000 tons of used fuel are currently in either dry or wet storage on site at nuclear power plants or at specified spent fuel storage facilities. Additionally, about 2,000 tons are added to that sum each year. To promote retrievability, limit degradation in the canister, and reduce potential for hydrogen generation, spent fuel that is initially in wet storage must be dried before moving to dry cask storage. Therefore, this research seeks to use a mock used fuel storage cask to replicate the conditions in the industry during the drying process of fuel being moved from storage pools. To simulate this, a mock storage cask has been constructed to closely resemble those used in industry such that any areas that tend to retain water are replicated effectively. It is able to hold one mock used fuel assembly. The assembly used is an AREVA 10A 10 x 10 assembly with 78 rods filled with depleted uranium, 12 heater rods to simulate decay heat, a 3 x 3 BWR water channel, and one interchangeable rod position for tests such as a simulated failed fuel rod, BWR water rod, and PWR guide tube tests. The industry standard procedure is used in drying the assembly which involves stepping the system pressure down and holding for certain periods until reaching 3 torr and holding for 30 minutes. This is believed to sufficiently dry the assembly. The goal is to record data throughout these tests that can provide the industry with a greater understanding of how well the process works. This data will also be used to develop and validate a computer model of the drying process. Mechanistic models are currently under development. Reduced order models are being investigated for incorporation into current related engineering codes such as COBRA-SFS. COBRA-SFS is a member of the COBRA family of codes which is mainly used in the evaluation of thermal hydraulic systems containing spent nuclear fuel. However, it does not currently have the capability to account for processes such as evaporation encountered during drying operations. Therefore, those physical models are to be added to the source code. At present a model of the experiment has been created using the COBRA-SFS Cycle-4 code. A number of steady state models along with transient models have been developed and presented. Each of these yield valuable results for comparison with the experiments conducted. In addition to comparison with experimental results, the data collected from COBRA-SFS is compared with that which has been generated from other computer models which utilize computational fluid dynamics. (authors)
Alternatives to rare earth permanent magnets, such as alnico, will reduce supply instability, increase sustainability, and could decrease the cost of permanent magnets, especially for high temperature applications, such as traction drive motors. Alnico magnets with moderate coercivity, high remanence, and relatively high energy product are conventionally processed by directional solidification and (significant) final machining, contributing to increased costs and additional material waste. Additive manufacturing (AM) is developing as a cost effective method to build net-shape three-dimensional parts with minimal final machining and properties comparable to wrought parts. This work describes initial studies of net-shape fabrication of alnico magnets by AM using a laser engineered net shaping (LENS) system. High pressure gas atomized (HPGA) pre-alloyed powders of two different modified alnico “8” compositions, with high purity and sphericity, were built into cylinders using the LENS process, followed by heat treatment. The magnetic properties showed improvement over their cast and sintered counterparts. The resulting alnico permanent magnets were characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD), and hysteresisgraph measurements. Furthermore, these results display the potential for net-shape processing of alnico permanent magnets for use in next generation traction drive motors and other applications requiring high temperatures and/or complex engineered part geometries.
Soils are heterogeneous and dynamic systems characterized by complex physical, chemical, and biological interactions. Understanding these interactions is critical, as they influence plant productivity, global biogeochemical cycles, and ecosystem resilience. While ecologists have long studied soils in field, greenhouse, and laboratory settings, their complexity and heterogeneity make it challenging to pinpoint key properties driving biological processes and derive mechanistic insights. Advancements in synthetic biology, which seeks to engineer and control biological processes in soils, have increased the demand for standardized and controllable experimental platforms. These platforms, referred to here as ‘synthetic soils’, are systems designed to reproduce selected physicochemical characteristics of natural soils in a simplified and defined format, allowing scientists to systematically change soil physicochemical properties (i.e. texture, mineralogy, pH) to study how biological components (i.e. microbes, plants, soil fauna, etc.) respond to, modify, or interact within these controlled environments. This review explores existing synthetic soils, their advantages, limitations, and applications in ecology and synthetic biology, and discusses potential directions for their future development.
The public has an ever-increasing interest in the economic, environmental, and social impacts of global energy production. To support informed decision making, the scientific community has a responsibility to communicate reliable and straight-forward information to the general public, in an engaging way, regarding energy systems and how choices made at different stages of an energy technology life cycle can impact the cost, amount of materials used, and waste produced. Our objective is to enhance public engagement via an interactive electrical energy game (Energ!ze) through which players interact with one another in their quest to develop an electrical energy portfolio that optimizes economic (e.g., company profit), environmental (e.g., reduced CO 2 emissions), and social (e.g., public opinion) impacts. The design and development of Energ!ze was an iterative process involving an interdisciplinary team of engineers, discipline-based scientists, computer scientists, and education experts. The development objectives were two-fold: (1) Accurate and quantifiable modeling of electrical energy systems and (2) Engaging and interactive user interface within the construct of the game narrative. The major game mechanics in the current playable Energ!ze version focus on economic metrics, and the database is scaffolded to add environmental and social metrics for future versions. This report details the outcome of the funding period (Section 2 and 3) and describes the development process, along with recommendations for future development (Section 4).
Being produced from organic matters of wastes (bio-wastes) through a fermentation process, biogas mainly composed of CH{sub 4} and CO{sub 2} and can be considered as a secondary energy carrier derived from solar energy. To generate electricity from biogas through the electrochemical process in fuel cells is a state-of-the-art technology possessing higher energy conversion efficiency without harmful emissions compared to combustion process in heat engines. Getting benefits from high operating temperature such as direct internal reforming ability and activation of electrochemical reactions to increase overall system efficiency, solid oxide fuel cell (SOFC) system operated with biogas becomes a promising candidate for distributed power generator for rural applications leading to reductions of environmental issues caused by greenhouse effects and bio-wastes. CO{sub 2} reforming of CH{sub 4} and electrochemical oxidation of the produced syngas (H{sub 2}–CO mixture) are two main reaction processes within porous anode material of SOFC. Here catalytic and electrochemical behavior of Ni-ScSZ (scandia stabilized-zirconia) anode in the feed of CH{sub 4}–CO{sub 2} mixtures as simulated-biogas at 800 °C were evaluated. The results showed that CO{sub 2} had strong influences on both reaction processes. The increase in CO{sub 2} partial pressure resulted in the decrease in anode overvoltage, although open-circuit voltage was dropped. Besides that, the simulation result based on a power-law model for equimolar CH{sub 4}−CO{sub 2} mixture revealed that coking hazard could be suppressed along the fuel flow channel in both open-circuit and closed-circuit conditions.
Residential homes and light commercial buildings usually require substantial heat and electricity simultaneously. A combined heat and power system enables more efficient and environmentally friendly energy usage than that achieved when heat and electricity are produced in separate processes. However, due to financial and space constraints, residential and light commercial buildings often limit the use of traditional large-scale industrial equipment. Here we develop a micro–combined heat and power system powered by an opposed-piston engine to simultaneously generate electricity and provide heat to residential homes or light commercial buildings. The developed prototype attains the maximum AC electrical efficiency of 35.2%. The electrical efficiency breaks the typical upper boundary of 30% for micro–combined heat and power systems using small internal combustion engines (i.e., <10 kW). Moreover, the developed prototype enables maximum combined electrical and thermal efficiencies greater than 93%. The prototype is optimally designed for natural gas but can also run renewable biogas and hydrogen, supporting the transition from current conventional fossil fuels to zero carbon emissions in the future. The analysis of the unit’s decarbonization and cost-saving potential indicate that, except for specific locations, the developed prototype might excel in achieving decarbonization and cost savings primarily in US northern and middle climate zones.
Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.
The core objectives of the Plug & Play PV Systems Project were to develop a PV system that can be installed on a residential rooftop for less than $1.50/W in 2020, and in less than 10 hours (from point of purchase to commissioning). The Fraunhofer CSE team’s approach to this challenge involved a holistic approach to system design – hardware and software – that make Plug & Play PV systems: • Quick, easy, and safe to install • Easy to demonstrate as code compliant • Permitted, inspected, and interconnected via an electronic process Throughout the three years of work during this Department of Energy SunShot funded project, the team engaged in a substantive way with inspectional services departments and utilities, manufacturers, installers, and distributors. We received iterative feedback on the system design and on ideas for how such systems can be commercialized. This ultimately led us to conceiving of Plug & Play PV Systems as a framework, with a variety of components compatible with the Plug & Play PV approach, including string or microinverters, conventional modules or emerging lightweight modules. The framework enables a broad group of manufacturers to participate in taking Plug & Play PV Systems to market, and increases the market size for such systems. Key aspects of the development effort centered on the system hardware and associated engineering work, the development of a Plug & Play PV Server to enable the electronic permitting, inspection and interconnection process, understanding the details of code compliance and, on occasion, supporting applications for modifications to the code to allow lightweight modules, for example. We have published a number of papers on our testing and assessment of novel technologies (e.g., adhered lightweight modules) and on the electronic architecture.
The natural subsurface is highly heterogeneous with minerals distributed in different spatial patterns. Fundamental understanding of how mineral spatial distribution patterns regulate sorption process is important for predicting the transport and fate of chemicals. Existing studies about the sorption was carried out in well-mixed batch reactors or uniformly packed columns, with few data available on the effects of spatial heterogeneities. As a result, there is a lack of data and understanding on how spatial heterogeneities control sorption processes. In this project, we aim to understand and develop modeling capabilities to predict the sorption of Cr(VI), an omnipresent contaminant in natural systems due to its natural occurrence and industrial utilization. We systematically examine the role of spatial patterns of illite, a common clay, in determining the extent of transport limitation and scaling effects associated with Cr(VI) sorption capacity and kinetics using column experiments and reactive transport modeling. Our results showed that the sorbed mass and rates can differ by an order of magnitude due to of the illite spatial heterogeneities and transport limitation. With constraints from data, we also developed the capabilities of modeling Cr(VI) in heterogeneous media. The developed model is then utilized to understand the general principles that govern the relationship between sorption and connectivity, a key measure of the spatial pattern characteristics. This correlation can be used to estimate Cr(VI) sorption characteristics in heterogeneous porous media. Insights gained here bridge gaps between laboratory and field application in hydrogeology and geochemical field, and advance predictive understanding of reactive transport processes in the natural heterogeneous subsurface. We believe that these findings will be of interest to a large number of environmental geochemists and engineers, hydrogeologists, and those interested in contaminant fate and transport, water quality and water composition, and natural attenuation processes in natural systems.
This work evaluates the Modular Hydrothermal Liquefaction System (MHTLS), an engineering-scale, integrated continuous HTL plant operated at the Pacific Northwest National Laboratory (PNNL), for converting realistic wet wastes into energy-dense biocrudes. The production campaigns discussed here processed algae, sewage sludges, lignocellulosic blends, Industrial food waste, and engineered food-waste slurries at 350?°C and around 200?bar, with nominal feed rates of ~12?L?h?¹. We report biocrude yields and composition, establish mass and elemental (C, N) balances, and quantify energy performance via heater duties, heat-exchanger behavior, and system-level efficiencies. Biocrudes contained 76–80?wt?% C (dry, ash-free) with HHVs of 38-41?MJ?kg?¹, substantially higher than feed materials HHVs of 16.6–26.1?MJ?kg?¹ and approaching petroleum fuels. Dry, ash-free biocrude yields of 32–53?wt?% corresponded to 43–71?wt?% carbon yields, with 18–40?wt?% of feed carbon routed to the aqueous phase. Thermal efficiencies were 50-65%, and total energy efficiencies, including reactor heat input, were 35-55%. A counter-current tube-in-tube heat exchanger delivered U values of 200–450?W?m?²?K?¹, with fouling-induced declines impacting heat recovery and heater duty. The analysis highlights three priorities for the process intensification of HTL: robust, fouling-resistant heat recovery, hydrodynamically suitable reactor and heat-exchanger designs, simplified and predictable solids management, and biocrude-water separation.
The aim of this work is to (1) introduce an approach, called polytope sector-based synthesis (PSS), for synthesizing 2D or 3D microstructural architectures that exhibit a desired bulk-property directionality (e.g., isotropic, cubic, orthotropic, etc.), and (2) provide general analytical methods that can be used to rapidly optimize the geometric parameters of these architectures such that they achieve a desired combination of bulk thermal conductivity and thermal expansion properties. Although the methods introduced can be applied to general beam-based microstructural architectures, we demonstrate their utility in the context of an architecture that can be tuned to achieve a large range of extreme thermal expansion coefficients—positive, zero, and negative. The material-property-combination region that can be achieved by this architecture is determined within an Ashby-material-property plot of thermal expansion versus thermal conductivity using the analytical methods introduced. These methods are verified using finite-element analysis (FEA) and both 2D and 3D versions of the design have been fabricated using projection microstereolithography.
Cooperative Research and Development Agreement (CRADA) NFE-17-06843 between Oak Ridge National Laboratory (ORNL) and SkyNano LLC focused on studying and developing a better fundamental understanding of SkyNano’s technology to capture and convert CO 2 into solid carbon nanostructures, including carbon nanotubes (CNTs), using electrochemistry at elevated temperatures in molten salts. The project focused on developing a fundamental scientific understanding of how electrochemistry influences the dynamic catalytic processes that drive the formation of CNTs on electrodes, while developing synthesis and processing technology relevant to implementation of the technology – including chemical engineering to aid in recapturing the lithium carbonate electrolyte essential to the process, and the modeling and testing of systems with the required thermal management at practical scales.
Lignocellulose is a promising feedstock for biofuel production as a renewable, carbohydrate-rich and globally abundant source of biomass. However, challenges faced include environmental and/or financial costs associated with typical lignocellulose pretreatments needed to overcome the natural recalcitrance of the material before conversion to biofuel. Anaerobic fungi are a group of underexplored microorganisms belonging to the early diverging phylum Neocallimastigomycota and are native to the intricately evolved digestive system of mammalian herbivores. Anaerobic fungi have promising potential for application in biofuel production processes due to the combination of their highly effective ability to hydrolyse lignocellulose and capability to convert this substrate to H2 and ethanol. Furthermore, they can produce volatile fatty acid precursors for subsequent biological conversion to H2 or CH4 by other microorganisms. The complex biological characteristics of their natural habitat are described, and these features are contextualised towards the development of suitable industrial systems for in vitro growth. Moreover, progress towards achieving that goal is reviewed in terms of process and genetic engineering. In addition, emerging opportunities are presented for the use of anaerobic fungi for lignocellulose pretreatment; dark fermentation; bioethanol production; and the potential for integration with methanogenesis, microbial electrolysis cells and photofermentation.