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DOE OSTI2022

Assessment of BQ-9000 Biodiesel Properties for 2021

This is the fifth in a series of reports documenting the quality of biodiesel from U.S. and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry voluntary quality assurance program. Participants agreed to provide monthly data on critical quality parameters for calendar year 2021. The quality data was provided to a team of experts, who removed any identifying company information and provided anonymized and randomized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. The critical quality parameters analyzed were: sodium and potassium (Na+K); calcium and magnesium (Ca+Mg); phosphorus (P); flash point and alcohol control; water and sediment; cloud point; acid number; free and total glycerin; monoglycerides; sulfur; oxidation stability; and cold soak filterability test (CSFT). The data was not weighted for production volume.

09 BIOMASS FUELS↗

DOE OSTI2021

Temperature controlled friction extrusion process

Processing temperature is decisive to the properties and performance of the product in friction extrusion. However, the constantly varying thermal boundary conditions result in an unstable processing temperature even fixed die rotational speed and feed rate were implemented, which extensively disturb the stability of the process and the quality of the product. It is key to maintain the process temperature to ensure the properties of the product is uniform. What's more, a controlled processing temperature enables the properties of the product to be manipulatable between parts and between different locations of one part

Li, Xiao↗

DOE OSTI2021

The case for digital twins in metal additive manufacturing

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

36 MATERIALS SCIENCE↗

DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Improved Performance of Cu(InGa)(SeS) 2 PV Modules Using the Reaction of Metal Precursors. Final Report

This project “Improved Performance of Cu(InGa)(SeS) 2 PV Modules using the Reaction of Metal Precursors” was a partnership led by the Institute of Energy Conversion (IEC) at the University of Delaware with Columbia University and the Molecular Foundry at the Lawrence Berkeley National Laboratory. The aim was to develop pathways to improve Cu(InGa)(SeS) 2 (CIGSS) thin film photovoltaic modules using processes compatible with low manufacturing cost. The CIGSS approach investigated was a two-step process including deposition of metal precursor films following by reaction in hydride gases utilizing IEC’s novel reactor. The process was similar to that under commercial development by the project’s industry partner Stion. When Stion went out of business mid-project the focus changed to a rapid thermal process considered more commercially viable. Approaches to improve the performance of solar cells using the reacted films focused on two material innovations. First, the overall Ga content was increased to increase the operating voltage, which is desirable for scale-up to commercial modules. Second, the processing and performance advantages arising from Ag alloying were investigated. Advanced characterization guided process and material development including control of relative composition gradients. Research on the formation of Cu-Ga-In metal precursors utilized sputtering deposition which is normally used in commercial applications. The work resulted in processes for deposition of precursor stacks with increased relative Ga content and effects of deposition parameters on morphology and phase composition were established. It was shown that the metal precursor films have comparable phase composition and morphology so subsequent reaction follows from the same starting point. The addition of Ag to the metal precursors gave more uniform morphology and improved adhesion of reacted films which enable higher reaction temperature for faster processing. A novel outcome was the discovery of a previously undocumented material phase in sputter-deposited and evaporated Ag-Cu-In-Ga thin films. Hydride gas reaction processes including time-temperature-concentration profiles were developed for different precursor compositions. This enables control of composition profiles to engineer through-film gradients for solar cell optimization with characterization and simulations used to correlate measured film composition profiles to measurements of devices. In particular, the gradient of sulfur at the front of the CIGSS film was found to be critical. The simulations guided process development leading to improved reproducibility of devices improved performance with higher Ga content and higher voltage. With Ag-alloyed precursors, the reaction pathways leading were determined. A significant finding was that Ag-alloying increases the reaction rate to completely convert precursor films to the final chalcopyrite which could enable reduced reaction time to benefit manufacturability. To maintain potential commercial viability, the process under investigation was refocused to a rapid thermal process that could potentially be incorporated into an in-line process for manufacturing. Precursors with different composition were capped with an extra selenium layer and reacted in hydrogen sulfide 5-15 minutes, compared to typically 2 hours in the previous multi-step batch process. Critical RTP parameters were identified to control the reaction. Further optimization would be needed for high efficiency solar cells but pathways to high quality devices with further optimization and improved heating uniformity were developed. The project also developed new optoelectronic characterization approaches with a focus on development and application of spatial- and time-resolved photoluminescence and a custom mapping photoluminescence microscope built. It was shown how critical electronic transport properties strongly depend on the chemical composition of the material and that a wide range of samples show inhomogeneity on a length scale larger than the grains in the films. Additionally, two-photon excitation capability was developed to distinguish bulk vs surface losses. The project advances the state-of-the -art for precursor reaction processes in several ways that could impact manufacturing. This includes validation of approaches to increase voltage and establishment of model-guided control to form optimal composition profiles. The application of process control approaches with knowledge of phase formation and reaction pathways can be critically valuable in designing a large-scale process.

14 SOLAR ENERGY↗

DOE OSTI2022

Impact of Load Tap Changer Control Operation Under Microgrid Conditions

Every microgrid is unique in its diversity of assets, operation, and network structure. This uniqueness presents challenges for each microgrid implementation that need to be understood and addressed to prevent power quality concerns, stability issues, and blackouts. In this research work, we present one challenge faced during microgrid operation that could potentially cause a blackout in some microgrid configurations. Through the incorrect operation of the load tap changer controller during an islanding event for a microgrid with generation present on both primary and secondary busses, the primary voltage will increase or decrease beyond allowable bounds, causing a cascading failure. This work presents the issue first with simulation results to demonstrate the phenomenon and then presents results of load tap changer controller operation using a controller-hardware-in-the-loop experiment. Finally, details of the complete sequence of operation and the blackout scenarios are presented and summarized.

blackout↗

DOE OSTI2024

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗

DOE OSTI2020

Correlations between thermal history and keyhole porosity in laser powder bed fusion

Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls – yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al4V powder.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Filament geometry control in extrusion-based additive manufacturing of concrete: The good, the bad and the ugly

Control of filament geometry in extrusion based additive manufacturing is essential to guarantee the desired quality of the printing process and the final product. Depending on the selected process parameters, material strategy and geometrical features, the printing process can however be susceptible to filament tearing or filament buckling. In this work, 2D and 3D CFD simulations have been used to map the influence of all these parameters in the infinite brick regime of 3D concrete printing processes. Analytical derivations of a tearing factor and buckling factor are presented. The use of these analytical tools is then illustrated, by studying variations in the printing process, such as changes in velocities, nozzle height, or material properties. Finally, the risk of both filament tearing and buckling of a single filament when printing sharp turns is discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗

DOE OSTI2020

Wintertime particulate matter decrease buffered by unfavorable chemical processes despite emissions reductions in China

Extreme and persistent haze events frequently occur during wintertime China. While recent emissions reductions reduced annual mean fine particulate matter (PM2.5) concentrations over eastern China, their effectiveness on wintertime PM2.5 trend remains uncertain. We use observations and model simulations to quantify seasonal differences in PM2.5 trends and investigate the underlying chemical mechanisms driving such differences. We find a much slower decrease in observed wintertime PM2.5 (–3.2% yr-1) since 2014, in contrast to a drastic summertime decrease (–10.3% yr-1). Simulations show two previously underappreciated mechanisms buffering wintertime PM2.5 decrease, including an increase in oxidation capacity due to nitrogen oxides (NOx) reductions under wintertime volatile organic compound (VOC)- limited chemistry, and an enhanced conversion of nitric acid to nitrate by ammonia due to sulfur dioxide reductions. Our findings suggest that control policies targeting VOC and deep NOx reductions are needed to improve wintertime PM2.5 air quality over China.

Leung, Danny M.↗

DOE OSTI2021

Low-divergence femtosecond X-ray pulses from a passive plasma lens

Electron and X-ray beams originating from compact laser-wakefield accelerators have very small source sizes that are typically on the micrometre scale. Therefore, the beam divergences are relatively high, which makes it difficult to preserve their high quality during transport to applications. To improve on this, tremendous efforts have been invested in controlling the divergence of the electron beams, but no mechanism for generating collimated X-ray beams has yet been demonstrated experimentally. Here we propose and realize a scheme where electron bunches undergoing focusing in a dense, passive plasma lens can emit X-ray pulses with divergences approaching the incoherent limit. Furthermore, compared with conventional betatron emission, the divergence of this so-called plasma lens radiation is reduced by more than an order of magnitude in solid angle, while maintaining a similar number of emitted photons per electron. This X-ray source offers the possibility of producing brilliant and collimated few-femtosecond X-ray pulses for ultra-fast science, in particular for studies based on X-ray diffraction and absorption spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Final Physics Design of Proton Improvement Plan-II at Fermilab

This paper presents the final physics design of the Proton Improvement Plan-II (PIP-II) at Fermilab, focusing on the linear accelerator (Linac) and its beam transfer line. We address the challenges in longitudinal and transverse lattice design, specifically targeting collective effects, parametric resonances, and space charge nonlinearities that impact beam stability and emittance control. The strategies implemented effectively mitigate space charge complexities, resulting in significant improvements in beam quality—evidenced by reduced emittance growth, lower beam halo, decreased loss, and better energy spread management. This comprehensive study is pivotal for the PIP-II project's success, providing valuable insights and approaches for future accelerator designs, especially in managing nonlinearities and enhancing beam dynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Final Physics Design of Proton Improvement Plan-II At Fermilab

This paper presents the final physics design of the Proton Improvement Plan-II (PIP-II) at Fermilab, focusing on the linear accelerator (Linac) and its beam transfer line. We address the challenges in longitudinal and transverse lattice design, specifically targeting collective effects, parametric resonances, and space charge nonlinearities that impact beam stability and emittance control. The strategies implemented effectively mitigate space charge complexities, resulting in significant improvements in beam quality -- evidenced by reduced emittance growth, lower beam halo, decreased loss, and better energy spread management. This comprehensive study is pivotal for the PIP-II project's success, providing valuable insights and approaches for future accelerator designs, especially in managing nonlinearities and enhancing beam dynamics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Tale of Two Domains: Cyber - Physical

As devices and systems continue to modernize and adopt integrated circuits, the use of cyber technology to deploy an application is the expectation. This deployment through cyber assets brings new cyber risk and cybersecurity is the practice of managing this risk. Cyber-risk is constantly changing due to the speed of technology advancement and the changing quality of the adversary. Cyber-Informed Engineering (CIE) mitigates cyber-risk through engineering controls where as the traditional practice of cybersecurity mitigates cyber-risk through cybersecurity controls. By clearly defining the cyber-physical boundary, engineering controls and cybersecurity controls can clearly demonstrate their complementary nature to provide layered defenses and successfully mitigate cyber-risk through independent controls. In this paper, a layered model of device decomposition of the the cyber-physical boundary is presented to provide clarity where engineering controls are used to reduce cyber-risk within the physics, functional materials, electronic, or integrated circuit layers and where cybersecurity controls are used to reduce cyber-risk within the machine code and application layers. By implementing both traditional cybersecurity controls and engineering controls, a more holistic approach to cybersecurity is achieved in protecting modern devices and systems, as well as a clear awareness in identifying, documenting, and authorizing the system’s cybersecurity protection scheme is achieved.

42 - ENGINEERING↗

DOE OSTI2020

Substrate quality drives fungal necromass decay and decomposer community structure under contrasting vegetation types

Fungal mycelium is increasingly recognized as a central component of soil biogeochemical cycling, yet our current understanding of the ecological controls on fungal necromass decomposition is limited to single sites and vegetation types. By deploying common fungal necromass substrates in a temperate oak savanna and hardwood forest in the midwestern USA, we assessed the generality of the rate at which high- and low-quality fungal necromass decomposes; further, we investigated how the decomposer ‘necrobiome’ varies both across and within sites under vegetation types dominated by either arbuscular or ectomycorrhizal plants. In this work, the effects of necromass quality on decay rate were robust to site and vegetation type differences, with high-quality fungal necromass decomposing, on average, 2.5 times faster during the initial stages of decay. Across vegetation types, bacterial and fungal communities present on decaying necromass differed from bulk soil microbial communities and were influenced by necromass quality. Moulds, yeasts and copiotrophic bacteria consistently dominated the necrobiome of high-quality fungal substrates. Synthesis. We show that regardless of differences in decay environments, high-quality fungal substrates decompose faster and support different types of decomposer micro-organisms when compared with low-quality fungal tissues. These findings help to refine our theoretical understanding of the dominant factors affecting fast cycling components of soil organic matter and the microbial communities associated with rapid decay.

59 BASIC BIOLOGICAL SCIENCES↗