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At least 559 records · Page 31

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗

Gas Turbine Hybrid Technology at NASA

NASA has been instrumental in shaping the research for Gas Turbine – Electric Hybrid Technology by in-house R&D and through industry partners. The Technology Readiness Level (TRL) is being advanced so the technology will find its way into products used by the flying public as well as developing the longer-term goals. The hybrid approach leads to having the advantage of both a gas turbine and electrification in addition to using both approaches in a synergistic way. This is a panel session and the presentation will describe the basics of hybrid, and what NASA is doing for Gas Turbine Hybrid Technology.

Gas Turbine↗

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE)

NASA is planning to return to the Moon in the mid-2020s as a stepping stone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support. The software used in APACHE was created by the JSC Engineering Directorate, in partnership with Buendea, powered by a custom Unreal Engine 5 (UE5.3, Epic Games) project. APACHE currently utilizes the HTC Vive Pro Eye in a wireless configuration for VR simulations. There are two virtual environments that subjects can explore within APACHE, a Lunar and Martian surface. The virtual Lunar surface was created from LIDAR data of the Lunar South Pole to create roughly 16 sq km of explorable terrain. The virtual Martian surface contains roughly 400 sq km of explorable terrain derived from Mars Reconnaissance Orbiter LIDAR data of the Jezero Crater. The immersion and related cognitive burdens of conducting a planetary EVA is simulated through a series of EVA-relevant tasks performed in the VR environment, using these high-fidelity visual representations. Additionally, APACHE includes biosensor driven informatics, such as real-time heart rate monitoring and/or derived values from model simulations, for active monitoring by the EV crew and added cognitive demand. A “Wizard of Oz” control panel enables test operators to activate contingency events such as simulated spacesuit malfunctions, loss of communications, and/or limited visibility. Embedded performance measures such as accuracy, completeness, and execution time have been developed for various exploration tasks to objectively quantify crew performance during an EVA and compare impacts to performance when different environmental stressors, both physical and cognitive, are added to or removed from the simulation. Additionally, validated cognitive and operational performance measures such as the Digit Symbol Substitution Task have been recreated and embedded in VR for direct and relatively unobtrusive measurement of motor perception. The APACHE environment currently supports multiple research studies at NASA. Examples include the CHAPEA project, a series of simulated year-long missions on Mars by a 4-person crew; and the CO2 Contingency Walk Back Study, an investigation of elevated CO2 exposure on crew performance during a contingency EVA scenario. APACHE also provides a test environment to support the development of the Crew State and Risk Model, which is a collection of individualized, mathematical models of crew physical and cognitive state; and the Personalized EVA Informatics and Decision Support system, an operational tool for flight controllers, and eventually a self-reliant Martian crew, to make biomedically-informed decisions in real-time to optimize the EVA planning and execution with respect to crew health and performance. Some technical challenges associated with developing the APACHE environment, as well as current limitations, include VR limitless natural walking with a hybrid spacesuit simulator, optimizing performance for wireless PC VR streaming while maintaining a high degree of visual fidelity, and the integration of various physiological (metabolic masks) and psychometric (eye tracking) sensors with the VR headset.

Human Performance↗

Lattice Confinement Fusion Fast Fission – A Hybrid Power System for Accessing Icy World Oceans

Lattice Confinement Fusion (LCF) is an emerging power technology that can be combined with nuclear fission to produce a hybrid innovative power system. The proposed innovation is a compact, scalable nuclear energy source that does not use highly enriched uranium (HEU), high-assay enriched uranium (HALEU), low enriched uranium (LEU) nor plutonium-238. The nuclear energy source consists of a hybrid fusion-fast-fission method whereby neutrons generated from LCF are used to fission materials such as depleted uranium or thorium. LCF has been demonstrated by both NASA (published in Physical Review C) and by Lawrence Berkeley National Laboratory (published in the Journal of Applied Physics). Although these methods are reminiscent of Low Energy Nuclear Reactions (LENR), both methods operate at much higher energies than any attempt at cold fusion. This new hybrid energy source is sufficient to provide power and heat for melting or boring through icy caps with untethered, autonomous probes. These probes can be used for planetary (i.e., Pluto), lunar (i.e., Enceladus), or asteroid (i.e., Ceres) exploration where icy caps are encountered. Each world may have a liquid water ocean beneath their ice crust. A robotic probe exploring the oceans beneath must either melt or bore through the ice crust first. Such a mission requires a small, but robust and long lived, electrical energy and heat source such as the LCF Fast Fission hybrid power system.

Theresa L. Benyo↗

Hybrid Analytics Solution for Power Plant Operations

Expert Microsystems, Inc., MapEx Software Inc. and XMPLR Energy LLC have teamed to submit a proposal to the US Department of Energy’s Office of Fossil Energy to improve the efficiency, reliability and flexibility of existing coal-based power plants. The project, titled Hybrid Analytics Solution to Improve Coal Power Plant Operations (the “Hybrid Analytics” project), will develop, demonstrate and commercialize a novel approach that integrates two proven real-time monitoring techniques. The Hybrid Analytics approach integrates into a single, integrated solution: a) an established, advanced data-driven analytics solution that includes artificial intelligence, advanced pattern recognition and machine-learning techniques and b) a well-proven, first principle thermal heat balance model solution. The future role of coal plants will depend on the ability to cycle and follow loads to meet marginal power demands, accommodate renewables and support the grid. Essential to success in this new role is a plant’s ability to maintain high reliability, efficiency and flexibility. However, these changing operational demands put significant stresses on power plant components that directly impact the ability to deliver these requirements. In the past, plants have independently monitored performance using physics-based solutions (heat rate, boiler losses, etc.) and reliability using data-driven analytics (advanced pattern recognition). The Hybrid Analytics solution integrates both approaches to enhance fault detection, provide automated diagnostics and estimates of remaining time to act, and provide critical, enhanced and accurate real-time information to coal plant operators. As a result, coal plants can improve efficiency, reliability, and flexibility and can cost-effectively maintain their key role in the power delivery system.

01 COAL, LIGNITE, AND PEAT↗

Multi-mode power split hybrid transmission with two planetary gear mechanisms

A multi-mode, power-split hybrid transmission system having two planetary gear (PG) sets connected to one engine, two electric motors, one output shaft, and each other by several clutches, brakes, and direct connection elements. Depending on the specific location and actuation of the various clutch and brake elements, the multi-mode, power-split hybrid transmission system can be run in one of several modes (e.g. electric drive, power-split, parallel hybrid, series hybrid, electronic continuously variable transmission (eCVT), generator, neutral, and the like).

33 ADVANCED PROPULSION SYSTEMS↗

Analysis of Using a Hybrid ZIRCEX Process to Generate High-Assay Low-Enriched Uranium (HALEU) - 20158

Idaho National Lab (INL) is investigating the feasibility of providing High-Assay Low Enriched Uranium (HALEU) by recovering Highly Enriched Uranium (HEU) from spent nuclear fuel (SNF) and downblending the HEU with natural, low-enriched, or depleted uranium (NU, LEU, or DU). That investigation identified that significant quantities of HALEU could be produced with a Hybrid ZIRCEX process to further advanced reactor research while reducing INL's environmental liability. Many new advanced reactor designs are being developed with improved safety, efficiency, and economics. Most require nuclear fuel with U-235 enrichment between 5% and 20%, which is defined as HALEU. Although technically feasible, there is no current domestic capability to make HALEU in the US. While it is anticipated that industry will provide HALEU through commercial enrichment once advanced reactors mature, until a market is established, an interim source of HALEU is needed to enable research and demonstration. An interim source of HALEU could be provided by recovering the HEU in some DOE-managed SNF and downblending it to between 5% and 20% using the Hybrid ZIRCEX Process. ZIRCEX is a dry head-end process to remove cladding (zirconium or aluminum) from SNF. Drying of the SNF is needed prior to introduction to ZIRCEX. After cladding removal, uranium and fission products in the bed material are oxidized and elutriated. In a Hybrid ZIRCEX process, the HEU process stream is sent to a very compact, modular solvent extraction process for uranium purification. The uranyl nitrate from solvent extraction is denitrated and calcined to produce a HALEU product ready for fuel fabrication. The long-lived fission products from solvent extraction are immobilized in glass using a small in-can melt. By removing the cladding before dissolution, the volume of glass waste is reduced by a factor of up to 300 times (when compared to not removing the cladding from the fuel). Other radioactive wastes from the process could be disposed of as Class A or Class B low-level waste. In addition to producing HALEU for advanced reactors, consumption of SNF would reduce DoE's environmental liability. Incorporating low-enriched spent fuel (versus exclusive use of LEU, DU, or NU) into the downblending scheme could reduce the SNF inventory even further. Several downblending and enrichment scenarios using INL fuels were investigated in this study. The results of this analysis show that the Hybrid ZIRCEX process could make a significant contribution to needed HALEU feedstock and reduce the environmental liability of managing SNF at INL. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗

Opportunities and Challenges for Nuclear-Renewable Hybrid Energy Systems

Nuclear-renewable hybrid energy systems (also known as integrated nuclear-renewable systems) are conceptual systems that have two or more energy inputs and produce two or more products. They maximize profitability, and usually equipment utilization, by adjusting between the products depending upon the availability of resources and market value. For example a nuclear-renewable hybrid energy system that produces electricity and hydrogen would maximize sales of electricity when its price is high and shift to hydrogen production when the electricity price is low. This presentation summarizes the results of several economic analyses of nuclear-renewable hybrid energy systems and they key overall conclusions from those analyses.

economic analysis↗

HOPP - Hybrid Optimization and Performance Platform

The Hybrid Optimization and Performance Platform, HOPP, is a wind + solar + battery + X design software for optimizing co-located, utility-scale hybrid plants down to the component level for different markets and technoeconomic objectives. Key technology and financial inputs to the HOPP model that inform the objective to be optimized are presented. The layout and performance integration is combined with optimal dispatch and full financial modeling within an optimization framework. With an example scenario, optimal sizing and layout results are shown in a sensitivity analysis of prices for two hybrid configurations.

batteries↗

Distributed Wind-Energy-Based Hybrids

Presentation defining distributed wind-based hybrids and introducing the Hybrid Optimization Performance Platform (HOPP) an open-source tool that helps design and optimize buildable hybrid power plants.

distributed wind-based hybrids↗

Molecular Layer Deposited Aluminum‐Based Hybrid Resist for High‐Resolution Nanolithography and Direct Ultra‐High Aspect Ratio Pattern Transfer

Inorganic-containing hybrid photoresists are critical for next-generation extreme ultraviolet (EUV) lithography and angstrom-era semiconductor miniaturization. However, associated conventional solution processing struggles to achieve ultrathin, uniform films with high conformality and compositional control, limiting overall patterning performance. Here, this study reports the systematic lithographic patterning characterization of an Al-based hybrid resist synthesized via vapor-phase molecular layer deposition (MLD), using the trimethylaluminum (TMA) metal precursor and the hydroquinone (HQ) aromatic organic linker. The resist supports both sub-20 nm high-resolution nanolithography and virtually infinite silicon plasma etch selectivity. Lithographic performance studied using electron beam lithography (EBL) as a proxy for EUV shows that, under an optimized process adopting post-exposure bake, the resist achieves the best resolution of 15.4 nm linewidth under stringent 1:1 line-space high-density patterning—limited only by the minimum beam size of the EBL system. The resist also shows wide dose latitude and balanced performance in resolution, roughness, and sensitivity. The infinite silicon etch selectivity, stemming from spontaneous aluminum oxyfluoride passivation layer formation, enables fabrication of micrometer-tall, 40 nm-wide silicon nanofin structures using only a ≈30 nm-thick resist layer, without no hard mask. These results highlight the potential of MLD-based hybrid resists for developing next-generation resist materials for micro/nanoelectronics manufacturing.

36 MATERIALS SCIENCE↗

Coupling of Adhesion and Anti-Freezing Properties in Hydrogel Electrolytes for Low-Temperature Aqueous-Based Hybrid Capacitors

Solid-state zinc-ion capacitors are emerging as promising candidates for large-scale energy storage owing to improved safety, mechanical and thermal stability and easy-to-direct stacking. Hydrogel electrolytes are appealing solid-state electrolytes because of eco-friendliness, high conductivity and intrinsic flexibility. However, the electrolyte/electrode interfacial contact and anti-freezing properties of current hydrogel electrolytes are still challenging for practical applications of zinc-ion capacitors. Here, we report a class of hydrogel electrolytes that couple high interfacial adhesion and anti-freezing performance. The synergy of tough hydrogel matrix and chemical anchorage enables a well-adhered interface between hydrogel electrolyte and electrode. Meanwhile, the cooperative solvation of ZnCl 2 and LiCl hybrid salts renders the hydrogel electrolyte high ionic conductivity and mechanical elasticity simultaneously at low temperatures. More significantly, the Zn||carbon nanotubes hybrid capacitor based on this hydrogel electrolyte exhibits low-temperature capacitive performance, delivering high-energy density of 39 Wh kg –1 at –60 °C with capacity retention of 98.7% over 10,000 cycles. With the benefits of the well-adhered electrolyte/electrode interface and the anti-freezing hydrogel electrolyte, the Zn/Li hybrid capacitor is able to accommodate dynamic deformations and function well under 1000 tension cycles even at –60 °C. This work provides a powerful strategy for enabling stable operation of low-temperature zinc-ion capacitors.

25 ENERGY STORAGE↗

First principles study of the stability and thermal conductivity of novel Li-Be hybrid ceramics

Hybrid Li-Be ceramics, combining both tritium (T) breeder (Li) and neutron multiplier (Be) for use as part of the fuel cycle of future nuclear fusion reactors are proposed. The development of such hybrid materials may reduce thermal gradients through better matching of thermal properties, mitigating the detrimental effects that may accompany traditional breeder systems while maintaining acceptable neutron multiplication and T breeding. Here, first-principles methods are used to investigate stability and thermal transport properties of a set of compounds containing both Li and Be. It is demonstrated that BeLi 2 O 4 Ge and BeLi 2 O 4 Si are mechanically stable and have formation energies comparable with leading candidates for solid state breeder materials, Li 2 TiO 3 , Li 2 ZrO 3 . It is also demonstrated that similar to the leading candidates, these compounds are insulators with thermal transport defined by phonons. The calculated thermal conductivity of BeLi 2 O 4 Ge is slightly higher compared to Li 2 TiO 3 or Li 2 ZrO 3 while in the BeLi 2 O 4 Si it is almost three times higher compared to the rest of compounds due to a higher phonon group velocities and increased phonon lifetimes. These results indicate that hybrid Li-Be ceramics offer a potential route towards better matching of thermal properties with minimal functional property degradation, thereby offering better overall fuel cycle performance.

36 MATERIALS SCIENCE↗

Review of Computer-Aided Manufacturing (CAM) strategies for hybrid directed energy deposition

Hybrid additive manufacturing intertwines both additive and subtractive manufacturing layer by layer to digitally fabricate parts with complex geometries, improved surface finish and tight dimensional accuracies, the sum of which is difficult to obtain with any single process. Computer-Aided Manufacturing (CAM) software is required to orchestrate the machine toolpathing for both the deposition as well as the machining processes and is crucial for the successful fabrication of high quality structures. Additionally, CAM requires substantial operator input to account for challenging aspects of each fabricated structure. For example, deciding at which layer of deposition will the machining process continue to maintain access to complex cavities for finishing - internal features that would otherwise be unfinishable due to reach limitations or obstructions. Moreover, of the many commercially-available hybrid systems, each has a unique kinematic environment which can benefit from specific optimization of toolpath planning and substantial research has directly correlated toolpathing with the microstructure evolution, mechanical properties, porosity and residual stress state of the final fabricated part. This review explores the available strategies for CAM in the context of hybrid direct energy deposition, discusses the advantages and disadvantages of each and considers future CAM trends for this transformational digital manufacturing technology.

36 MATERIALS SCIENCE↗