Results for “Design Process” — Page 96

DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Using a system’s equilibrium behavior to reduce its energy dissipation in nonequilibrium processes

Significance Biomolecular machines implement many vital activities in cells and must operate quickly and in functional directions, requiring energy dissipation. Recent experiments reveal that some evolved machines are quite energetically efficient, engendering interest in design principles that achieve such high efficiency. Recent theory predicts how to use equilibrium measurements (in the absence of driving) to design ways to drive a system to minimize energy dissipation. Here, we experimentally demonstrate the utility of this theory for designing efficient driving processes (“protocols”) by rapidly unfolding and folding single DNA hairpins. We show that such designed protocols systematically and significantly reduce energy dissipation over large variations of driving speed and DNA hairpin friction. Similar protocols may underlie the high efficiency observed in molecular machines.

Tafoya, Sara↗

DOE OSTI2020

Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

42 ENGINEERING↗

DOE OSTI2024

Approach to startup inventory for viable commercial fusion power plant

With the increasing efforts to commercialize fusion power, private and government organizations are investing heavily in the development of technology to support a viable fusion power plant. Deuterium-Tritium (DT) fueled reactors are more prevalent than other proposed designs, requiring tritium processing and handling technology for safe operations and self-sufficiency. Further, each fusion power plant will need a specific-to-design startup inventory of tritium to begin operations. This startup inventory is required prior to breeding and is the minimum tritium inventory required to fill each processing component in the fuel cycle, to offset radioactive decay losses, and to avoid a zero-fuel situation for continuous operation. We present an approach to calculate the startup tritium inventory for a 500 MW th reactor, with considerations for reserve inventory for maintenance and commissioning. A baseline startup inventory was calculated to be approximately 327 gs. This value was obtained using modest assumptions about the technology and operating parameters of a fusion power plant. The required operating reserve inventory or the inventory necessary to keep a fusion power plant operational using only direct internal recycling for 24 h for the same plant design is approximately 642 gs. The approach and findings of this paper will enable fusion energy stakeholders to better utilize the existing scarce global tritium supply.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Utilizing geothermal heat for hydrothermal liquefaction of algae

The distribution of utilizable geothermal reservoirs in the US is concentrated in areas of low population density. In order to increase the scale of geothermal operations, we proposed to develop a modular biofuel production facility that directly uses geothermal heat. By transforming geothermal heat to chemical potential energy (rather than electricity), the scale of existing or new geothermal operations can be increased and distributed to areas of higher population density. Simultaneously, the economics of producing biocrude oil is improved significantly where geothermal heat is used, instead of natural gas-fired systems for providing heat and power. In the Phase I project, we demonstrated that it was economically feasible to sustainably produce a biofuel by integrating geothermal heat. It was found that, for the best case scenario at a scale of 10T(biomass)/hr, it was possible to produce a biofuel for $0.26/L, as compared to crude oil at $0.3/L (current selling price). Ultimately this demonstrates that this technology can be economically sustainable when integrated with geothermal heat. We also demonstrated that the addition of modular biofuel-production technologies can be more profitable for a geothermal plant operator. For 1000kg/day scale, it was found that it could be 2.5 times more profitable for a geothermal plant operator to sell bio-oil rather than electricity (with a waste source within 20 miles of the processing facility). Furthermore, as the design of the proposed technology recycles significant heat, requiring between 5% and 20% of the total heat load of the biofuel process to be supplied by the geothermal plant, it is feasible for an operator to initially implement the technology at small scale (i.e. less than 100gal/day) with the plans of scaling up operations in the future. Implementing the technology incrementally may minimize initial the investment and financial risk for the operator. Finally, in the Phase I project, we built and tested a hydrothermal system developed at ARI. We tested three different feedstock types, pure algae feed, algae mixed with manure, and algae with in-situ catalysts (molybdenum trioxide), over a range of temperatures and residence times. In terms of oil products, our results (yield, process times and elemental balance) were similar to what was obtained at PNNL. As such our experiments matched well with the techno-economic design (mass throughput specifications), and will thus well inform the Phase II process design. The experimental data obtained on the hydrothermal treatment of algae indicated that the highest recovery of oil occurred at 250°C. This shows that HTL can be carried out 100°C lower than what is currently considered ‘state of the art’, and thus save significant energy savings. Further, it was found that oil-recovered fractions had lower amounts of nitrogen at lower temperatures, indicating that oil produced at 250°C is of higher quality than if we were to produce fuel at 350°C.

09 BIOMASS FUELS↗

DOE OSTI2025

ARCADE Analysis Methods & Validation Pathway

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) provides an automated analysis system which supports risk-informed performance based (RIPB) evaluations of nuclear control systems. Every possible cyber threat which could lead to consequence is identified by simulating the unsafe control action sequences which transform digital harm into physical harm. Eliminating the simulation of complex digital cyber attack chains cuts out unnecessary computational overhead and focuses directly on the physics of cyber-physical attacks. This focus enables designers to make informed decisions which can entirely eliminate categories of cyber threats against advanced reactors through the physical nature of the plant design. This narrowing of cyber threat against nuclear power plants through the physics of the system is intended to make any remaining threat management and cost efficient. This is the goal of the Tiered Cyber Analysis (TCA) outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors. ARCADE has been custom developed to meet the demands of the rigorous analysis required in Tier 1 of the TCA, which forms the foundation of the TCA process. Currently, ARCADE is still under development, but has made significant leaps in capability. A pilot analysis on the opensource Asherah simulator was performed which demonstrated key functionality goals. The next stage of ARCADE development involves improvements to the applications which support the analysis system, and enabling the analysis system to utilize the full suite of unsafe control action simulations. Since the analysis method’s core functions are complete, validation of the analysis method will be started concurrent to the next development stages. The automated analysis ARCADE will provide can radically change the cybersecurity design process for advanced reactors, reducing the cost of security implementation while enhancing cyber resilience. The pathway for ARCADE’s development to this goal has become much clearer. The majority of technical hurdles have been cleared, and the remaining development needs have been solidified. ARCADE is now capable of assisting the advanced reactor design process and directly support advanced reactor industry RIPB practices.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

Structure Property Studies for Additively Manufactured Parts

Since the invention of modern Additive Manufacturing (AM) processes engineers and designers have worked hard to capitalize on the unique building capabilities that AM allows. By being able to customize the interior fill of parts it is now possible to design components with a controlled density and customized internal structure. The creation of new polymers and polymer composites allow for even greater control over the mechanical properties of AM parts. One of the key reasons to explore AM, is to bring about a new paradigm in part design, where materials can be strategically optimized in a way that conventional subtractive methods cannot achieve. The two processes investigated in my research were the Fused Deposition Modeling (FDM) process and the Direct Ink Write (DIW) process. The objectives of the research were to determine the impact of in-fill density and morphology on the mechanical properties of FDM parts, and to determine if DIW printed samples could be produced where the filament diameter was varied while the overall density remained constant.

42 ENGINEERING↗

DOE OSTI2018

Crossing the great divide between single-crystal reactivity and actual catalyst selectivity with pressure transients

The quantitative prediction of catalyst selectivity is essential to the design of efficient catalytic processes and requires a detailed knowledge of the reaction mechanism and rate constants. Here we present a study that accurately predicts, using the kinetics and a mechanism derived from fundamental studies on single-crystal gold, the product distribution resulting from the complex reaction network that governs the oxidative coupling of methanol, catalysed by nanoporous gold between 360 and 425 K and for a vast range of pressures. Analysis of the transient product responses to micropulses of methanol over nanoporous gold yields a precise understanding of the marked dependence of selectivity on pressure, surface oxygen coverage and temperature. The key to a high selectivity for methyl formate is the surface lifetime and abundance of the methoxy. In conclusion, this successful microkinetic modelling of catalytic reactions across a wide set of reaction conditions is broadly applicable to predicting catalytic selectivity and provides a pathway to designing more efficient catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Dynamic Diketoenamine Crosslinking Unlocks Vinyl Polymer Vitrimers From β ‐Triketone Chemistry

Covalent adaptable networks (CANs) offer a compelling strategy to unite the mechanical robustness of thermosets with the reprocessability of thermoplastics, yet achieving simultaneous durability, processability, and true recyclability remains challenging. We introduce diketoenamine (DKE) vitrimers derived from β-triketone methacrylate monomers and demonstrate how rational monomer design dictates network processability, viscoelasticity, and recyclability. By systematically varying the spacer length between the β-triketone (TK) moiety and the polymer backbone, we identify a key structure–property relationship that dictates vitrimer behavior. Networks bearing TK pendants minimally displaced from the backbone suppress creep but exhibit limited stress relaxation, whereas extended spacers yield lower glass transition temperatures, higher effective crosslink densities, and efficient stress dissipation, enabling optical transparency and reprocessability. Extending this platform to ultra-high molecular-weight prepolymers introduces physical entanglements as secondary crosslinks, further enhancing dimensional stability without compromising processability. Both mechanical and chemical recycling validate the closed-loop circularity of these materials. Furthermore, these results establish TK methacrylates as a versatile platform for designing high-performance vitrimers that integrate durability, reprocessability, and true closed-loop recyclability.

closed-loop recyclability↗

DOE OSTI2020

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

The development of usable visualization solutions is essential for ensuring both their adoption and effectiveness. User-centered design principles, which involve users throughout the entire development process, have been shown to be effective in numerous information visualization endeavors. We describe how we applied these principles in scientific visualization over a two year collaboration to develop a hybrid in situ/post hoc solution tailored towards combustion researcher needs. Lastly, we examine the importance of user-centered design and lessons learned over the design process in an effort to aid others seeking to develop effective scientific visualization solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Investigation on gate oxide reliability under gate bias screening for commercial SiC planar and trench MOSFETs

This paper evaluates the impact of the fast high gate-voltage screening technique on gate oxide reliability of commercial 1.2 kV 4H-SiC power metal-oxide-semiconductor field-effect transistors (MOSFETs) with planar and trench gate structures. The measurements are conducted on packaged devices with the intent that the results will be applicable to wafer-level screening. The threshold voltage (V th ) of SiC MOSFETs is measured before and after various screening treatments and recovery process. In addition, constant-voltage time-dependent dielectric breakdown (TDDB) measurements are performed on SiC MOSFETs to obtain the intrinsic lifetime of gate oxide. The objective of this study is to determine the optimal screening conditions and improve screening efficiency without degradation of gate oxide reliability, such as V th shift and reduced oxide intrinsic lifetime. Due to the differences in the gate oxidation process and structural design of SiC planar and trench MOSFETs, the two types of devices exhibit different oxide reliability in the screening process. The recommended screening conditions obtained in this paper reveal that SiC trench MOSFETs can accept higher screening voltages compared to SiC planar MOSFETs. Hence, it can be concluded that more efficient screening techniques can be adopted for SiC MOSFETs with thicker gate oxide to meet the requirements of industrial applications.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Spark plasma sintering of structure-tailored ultrahigh-temperature components: First step to complex net shaping

The net-shaping capability in spark plasma sintering (SPS) of ultrahigh-temperature ceramics has been explored in this study. The annular or ring-like shape is considered here as the first step to a more complex geometry compared to a solid cylinder or disk. ZrC powders have been SPS processed in specially designed graphite tooling to achieve the annular shape geometry. Experimental runs have been carried out to determine the optimal processing parameters for producing highly dense ZrC specimens in this geometry. Finite-element modeling framework has been constructed to determine the internal stress evolution, as well as densification, during the SPS of the annular-shaped ZrC. The formulated processing schemes for ZrC have been adapted to process SiC/ZrB2 composite powder with the purpose of making tooling components for SPS applications. The applicability of the obtained composite SPS tooling has been evaluated at high temperature and high pressure associated with SPS regimes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Investigation of Rare Earth Elements Mobility Using Molecular Dynamics

Rare Earth Elements (REE) are critical many applications such as electronics, permanent magnets, agricultural etc. Effective separation of REEs is a challenge due to their similarities in chemical and physical properties. Current separation processes are costly and environmentally detrimental. Therefore, research on REEs mobility in water-based solvents is necessary to design effective separation processes. In this research, the key kinetic and thermodynamic parameters, including diffusion coefficient and hydration shell are determined under the influence of external electric field. The diffusion coefficient of metal ions increases due to electric field in pure water.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

A Scalable Method for Decarbonizing Modular Building Solutions: Preprint

The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.

affordable housing↗

DOE OSTI2022

Investigation of Rare Earth Elements Mobility Using Molecular Dynamics

Rare Earth Elements (REE) are critical many applications such as electronics, permanent magnets, agricultural etc. Effective separation of REEs is a challenge due to their similarities in chemical and physical properties. Current separation processes are costly and environmentally detrimental. Therefore, research on REEs mobility in water-based solvents is necessary to design effective separation processes. In this research, the key kinetic and thermodynamic parameters, including diffusion coefficient and hydration shell are determined under the influence of external electric field. The diffusion coefficient of metal ions increases due to electric field in pure water.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

A Scalable Method for Decarbonizing Modular Building Solutions

The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.

affordable housing↗

DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.

14 SOLAR ENERGY↗

DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.

14 SOLAR ENERGY↗