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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Load balancing for multi-beam additive manufacturing systems

As powder bed fusion (PBF) additive manufacturing (AM) becomes a more mature field, system configurations are gradually moving away from the classic single heat source, layer-by-layer system configurations towards unconventional system configurations that offer higher throughput. Higher throughput systems allow PBF systems to be considered for a larger variety of industrial applications. However, the inclusion of multiple heat sources, or beams, also increases the complexity of the control schemes needed. For multi-beam systems with overlapping fields of view, the distribution of workload, or load balancing, across these beams directly affects the total print time for a build. Additionally, the probability of any beam failing in a multi-beam system increases with the number of beams. While manual methods of load balancing and dealing with beam failures are reasonable for current generation multi-beam systems, as system configurations become more complex, manual methods will become prohibitively inefficient. Here, this paper introduces two different ways to load balance multi-beam systems of various configuration types, regardless of their complexity, which are highly performant. A consequence of this performance is the enablement of on-the-fly load balancing in the event a beam fails, thus improving system robustness.

36 MATERIALS SCIENCE↗

DOE OSTI2021

An efficient reconstruction algorithm for diffusion on triangular grids using the nodal discontinuous Galerkin method

High-energy-density (HED) hydrodynamics studies such as those relevant to inertial confinement fusion and astrophysics require highly disparate densities, temperatures, viscosities, and other diffusion parameters over relatively short spatial scales. This presents a challenge for high-order accurate methods to effectively resolve the hydrodynamics at these scales, particularly in the presence of highly disparate diffusion. A significant volume of engineering and physics applications use an unstructured discontinuous Galerkin (DG) method developed based on the finite element mesh generation and algorithmic framework. This work discusses the application of an affine reconstructed nodal DG method for unstructured grids of triangles. Solving the diffusion terms in the DG method is non-trivial due to the solution representations being piecewise continuous. Hence, the diffusive flux is not defined on the interface of elements. The proposed numerical approach reconstructs a smooth solution in a parallelogram that is enclosed by the quadrilateral formed by two adjacent triangle elements. The interface between these two triangles is the diagonal of the enclosed parallelogram. Similar to triangles, the mapping of parallelograms from a physical domain to a reference domain is an affine mapping, which is necessary for an accurate and efficient implementation of the numerical algorithm. Thus, all computations can still be performed on the reference domain, which promotes efficiency in computation and storage. This reconstruction does not make assumptions on choice of polynomial basis. Reconstructed DG algorithms have previously been developed for modal implementations of the convection–diffusion equations. However, to the best of the authors’ knowledge, this is the first practical guideline that has been proposed for applying the reconstructed algorithm on a nodal discontinuous Galerkin method with a focus on accuracy and efficiency. As a result, the algorithm is demonstrated on a number of benchmark cases as well as a challenging substantive problem in HED hydrodynamics with highly disparate diffusion parameters.

Computational efficiency↗

DOE OSTI2024

On hydromagnetic wave interactions in collisionless, high- β plasmas

We describe the interaction of parallel-propagating Alfvén waves with ion-acoustic waves and other Alfvén waves, in magnetized, high- β collisionless plasmas. This is accomplished through a combination of analytical theory and numerical fluid simulations of the Chew–Goldberger–Low (CGL) magnetohydrodynamic (MHD) equations closed by Landau-fluid heat fluxes. An asymptotic ordering is employed to simplify the CGL-MHD equations and derive solutions for the deformation of an Alfvén wave that results from its interaction with the pressure anisotropy generated either by an ion-acoustic wave or another, larger-amplitude Alfvén wave. The difference in time scales of acoustic and Alfvénic fluctuations at high- β means that interactions that are local in wavenumber space yield little modification to either mode within the time it takes the acoustic wave to Landau damp away. Instead, order-unity changes in the amplitude of Alfvénic fluctuations can result after interacting with frequency-matched acoustic waves. Additionally, we show that the propagation speed of an Alfvén-wave packet in an otherwise homogeneous background is a function of its self-generated pressure anisotropy. This allows for the eventual interaction of separate co-propagating Alfvén-wave packets of differing amplitudes. The results of the CGL-MHD simulations agree well with these predictions, suggesting that theoretical models relying on the interaction of these modes should be reconsidered in certain astrophysical environments. Applications of these results to weak Alfvénic turbulence and to the interaction between the compressive and Alfvénic cascades in strong, collisionless turbulence are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Development of a data-driven neural network model for electron thermal transport in NSTX

A data-driven electron thermal transport neural network (ETT-NN) model, trained on TRANSP interpretative analysis results of National Spherical Torus Experiment (NSTX), was developed to enable faster and more accurate ETT computation for spherical tokamaks (STs). The model incorporates both convolutional NNs and recurrent NNs, allowing it to simultaneously account for the spatial and temporal non-localities and multi-scale features of turbulent transport, which have been considered only in a limited manner in conventional models. The model was validated through interpretative analysis and predictive simulations using Tokamak Reactor Integrated Automated Suite for Simulation and Computation, demonstrating relatively high accuracy. Additionally, parameter scans were performed on test discharges known to exhibit specific turbulent modes, such as microtearing mode, trapped electron mode, kinetic ballooning mode, and electron temperature gradient mode. The scanning results revealed that the ETT-NN model exhibits the same trends as those observed in conventional gyrokinetic simulations or theories, while also capturing the global nature of turbulent transport, indicating that the data-driven model accurately reflects the underlying physical characteristics. Furthermore, due to the dimensionless nature of the model, we can feasibly expand its applicability by incorporating data from other devices and uncovering the characteristics of ETT in STs in the future.

NSTX↗

DOE OSTI2020

Nonlinear self-focusing in strongly magnetized pair plasma

An intense radiation field can modify plasma properties and the corresponding refractive index and lead to nonlinear propagation effects such as self-focusing. In this work, we estimate the corresponding effects in pair plasmas for circularly polarized waves, in both unmagnetized and strongly magnetically dominated cases. First, in the unmagnetized pair plasma the ponderomotive force does not lead to charge separation but to density depletion. Second, for astrophysically relevant plasmas of pulsar magnetospheres [and possible loci of fast radio bursts (FRBs)], where the cyclotron frequency $ω_B$ dominates over the plasma frequency $ω_p$ and the frequency of the electromagnetic wave $ω_B \gg ω_p, ω$, we show that (i) there is virtually no nonlinearity due to changing effective mass in the field of the wave; (ii) the ponderomotive force is $F^{(B)}_p = –m_ec^2/4B^2_0∇E^2$, which is reduced by a factor $(ω/ω_B)^2$ if compared to the unmagnetized case ($B_0$ is the external magnetic field and $\textit{E}$ is the electric field of the wave); and (iii) for a radiation beam propagating along a constant magnetic field in the pair plasma with density $n_±$, the ponderomotive force leads to the appearance of circular currents that lead to a decrease of the field within the beam by a factor $ΔB/B_0 = 2πn_±m_ec^2E^2 /B^4_0$. Applications to the physics of FRBs are discussed; we conclude that for the parameters of FRBs, the dominant magnetic field completely suppresses nonlinear self-focusing or filamentation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Non-Equilibrium Processes, Stability, Design and Control of Pulsed Plasmas for Materials Processing

The energy and angle of impact of ions, interspersed with fluxes of neutral radicals, on the surface of wafers during plasma materials processing are critical parameters for the fabrication of microelectronic devices, nanostructure materials and biocompatible materials. This sensitivity extends to nearly all applications of plasmas to materials processing and surface modification. Low temperature plasmas for plasma materials processing have traditionally used continuously excited plasmas. However, pulsed plasma processing is an enabling technology for achieving finer resolution of microelectronics fabrication and for producing unique surface properties in a wide range of applications. This research project is addressing fundamental plasma physics issues in materials processing – the control of pulsed plasmas and the means for optimizing plasma produced reactant fluxes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1991

A contribution to laser range imaging technology

The goal of the project was to develop a methodology for fusion of a Laser Range Imaging Device (LRID) and camera data. Our initial work in the project led to the conclusion that none of the LRID's that were available were sufficiently adequate for this purpose. Thus we spent the time and effort on the development of the new LRID with several novel features which elicit the desired fusion objectives. In what follows, we describe the device developed and built under contract. The Laser Range Imaging Device (LRID) is an instrument which scans a scene using a laser and returns range and reflection intensity data. Such a system would be extremely useful in scene analysis in industry and space applications. The LRID will be eventually implemented on board a mobile robot. The current system has several advantages over some commercially available systems. One improvement is the use of X-Y galvonometer scanning mirrors instead of polygonal mirrors present in some systems. The advantage of the X-Y scanning mirrors is that the mirror system can be programmed to provide adjustable scanning regions. For each mirror there are two controls accessible by the computer. The first is the mirror position and the second is a zoom factor which modifies the amplitude of the position of the parameter. Another advantage of the LRID is the use of a visible low power laser. Some of the commercial systems use a higher intensity invisible laser which causes safety concerns. By using a low power visible laser, not only can one see the beam and avoid direct eye contact, but also the lower intensity reduces the risk of damage to the eye, and no protective eyeware is required.

Defigueiredo, Rui J. P.↗

DOE OSTI2025

Ultrasonic characterization of material heterogeneities in stainless steel components produced by laser powder bed fusion

We introduce pulse-echo ultrasound as a method for characterizing the impact of powder bed fusion parameters on the properties of additively manufactured stainless-steel components, their material anisotropy, and location-dependent heterogeneity. Our results indicate that accurate characterization requires careful selection of ultrasonic propagation paths, which must consider the direction of additive layering, variations in processing parameters, and the component's geometry. We employed two distinct methods to estimate material properties from ultrasonic data: One assumes isotropy, while the other accounts for anisotropic interactions during the propagation of elastic waves. When applied to samples fabricated with laser energy densities ranging from 24 to 42 J/mm³ , these methods revealed transverse isotropy and weak anisotropy (quantified by small Thomsen parameters, ε = 0.0651 and γ = 0.0092) and less than a ∼6 % change in acoustic impedance. The assumption of isotropy, in this case, leads to small errors (less than 4 % or 1 % for Young's modulus in the build or transverse directions) when estimating orthotropic material properties using ultrasonic data measured along just two orthogonal directions, one of which must align with the build direction. By comparing ultrasonic measurements — which aggregate the spatial variability in material properties along the length of elastic wave propagation into a single value — with localized measurements obtained from surface nanoindentation, we uncovered and spatially profiled significant differences between the surface and interior properties. Specifically, the surface Young's modulus decreased from approximately 210 GPa to 180 GPa within a depth of about 3 mm. We attribute this surface-localized heterogeneity in PBF-fabricated components to distinct thermal histories experienced by the surface and interior regions. Collectively, the results of this study establish a framework for the ultrasonic characterization of material heterogeneity and anisotropy in material properties and demonstrate its application in additively manufactured metal components.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Thermomechanical Behavior of Advanced Manufactured Parts, Subcomponents, and Their Weldments for Gen3 CSP

Generation 3 (Gen3) concentrating solar power (CSP) plants may require the use of molten chloride salt storage systems, solar receivers, and supercritical-CO 2 primary heat exchangers (PHX). The temperatures that would be expected in these parts and subcomponents could approach 760°C for hot side and 500°C for cold side at peak operating conditions. With the design limitations, highly corrosion- and creep-resistant alloys are needed for maximizing component lives. This report presents the results for the project "Thermomechanical Behavior of Advanced Manufactured Parts, Subcomponents, and their Weldments for Gen3 CSP", award number DE-EE00036334. In this project, creep enhanced ferritic alloy Grade 91, austenitic stainless-steel (SS) 304H, Ni claddings Ni201 and C22, and nickel-based superalloys Inconel 740H and Haynes 282 and 230 were evaluated for potential applications in Gen3 CSP systems. Advanced manufacturing of these parts, subcomponents and their welds, was investigated and a full technoeconomic analysis was made in comparison to conventional manufacturing techniques. The manufacturing techniques explored are explosion clad welding and combustion synthesis/combustion reaction for transfer pipes, additive manufacturing (AM) including laser-powder bed fusion (L-PBF) and electron beam AM (EBAM) with wire feedstock for PHX and solar receivers, and conventional fusion welding for similar and dissimilar joining of these various parts and subcomponents.

14 SOLAR ENERGY↗

DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Safe Autonomous Flight Environment (SAFE50) for the Notional Last 50 ft of Operation of 55 lb Class of UAS

The most difficult phase of small Unmanned Aerial System (sUAS) deployment is autonomous operations below the notional 50 ft in urban landscapes. Understanding the feasibility of safely flying sUAS autonomously below 50 ft is a game changer for many civilian applications. This paper outlines three areas of research currently underway which address key challenges for flight in the urban landscape. These are: (1) Off-line and On-board wind estimation and accommodation; (2) Real-time trajectory planning via characterization of obstacles using a LIDAR; (3) On-board information fusion for real-time decision-making and safe trajectory generation.

Autonomy↗

NASA NTRSDate not supplied

All-sky Retrieval of Atmospheric Temperature, Water Vapor, Clouds, Trace Gases, and Surface Properties from Operational Hyperspectral IR Sounders

Operational IR sounders such AIRS, CrIS, and IASI provide high quality hyperspectral measurements for weather and climate applications. We will describe a new all-sky Single Field-of-view Sounder Atmospheric Product (SiFSAP). The uniqueness of this product is that it uses all available channels from hyperspectral sounders and the optimal estimation retrieval is done at a single FOV spatial resolution. The SiFSAP includes atmospheric temperature, water vapor, clouds, trace gases, surface skin, and surface emissivity and will be produced operationally at NASA GES DISC. We will describe the core component of the SiFSAP algorithm, which is the Principal Component-based Radiative Transfer Model (PCRTM), and will show example applications of the SiFSAP product for various atmospheric weather and dynamics studies. We also describe a new Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides fast and accurate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

pcrtm↗

DOE OSTI2026

Effect of directed energy deposition process parameter on build quality of tantalum

Tantalum is a refractory metal used in a variety of harsh environment applications. Additive manufacturing of tantalum is limited based on its high melting point and affinity for oxygen. Directed Energy Deposition is an additive manufacturing technique with rapid deposition time and compositional flexibility within builds. Additively manufactured tantalum is susceptible to a variety of material and process-based defects. Various lack-of-fusion defects were identified resulting from excessive powder feed rates or insufficient laser power. Oxygen impurities in some samples caused cracking and increased material hardness. Precipitates were identified in the highly oxidized samples which were printed immediately after the build chamber was opened. High-density, low-defect parts were successfully produced. The effects of scan speed, laser power, and powder feed rate on density and defects were analyzed. A processing window was identified for producing high-quality parts which requires adequately high laser power and lower powder feed rate.

DED↗

DOE OSTI2024

High absorptivity nanotextured powders for additive manufacturing

The widespread application of metal additive manufacturing (AM) is limited by the ability to control the complex interactions between the energy source and the feedstock material. Here, we develop a generalizable process to introduce nanoscale grooves to the surface of metal powders which increases the powder absorptivity by up to 70% during laser powder bed fusion. Absorptivity enhancements in copper, copper-silver, and tungsten enable energy-efficient manufacturing, with printing of pure copper at relative densities up to 92% using laser energy densities as low as 83 joules per cubic millimeter. Simulations show that the enhanced powder absorptivity results from plasmon-enabled light concentration in nanoscale grooves combined with multiple scattering events. The approach taken here demonstrates a general method to enhance the absorptivity and printability of reflective and refractory metal powders by changing the surface morphology of the feedstock without altering its composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

LDRD FY25 Program Overview

As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2021

A second order yield-temperature relation for accurate inference of burn-averaged quantities in multi-species plasmas

Measured yields and ion temperatures inferred from the fusion product energy spectra can be used as metrics for the performance of an ICF implosion. This can be used to infer species separation, thermal decoupling, flows, or other effects that can cause the inferred ion temperatures to deviate from the true underlying thermal temperature and the yield ratio to deviate from the expected value. Direct inference of the impact of these effects on observed temperatures and yields can be difficult to uncover due to the underlying dependence on the shape and time evolution of the temperature and density profiles of the fusing plasma. Due to differences in the temperature dependence of the reactivities, different fusion products are emitted from different regions and times within the implosion. In order to properly account for this, a second-order analytical expression relating the apparent temperatures and yield ratios is developed. This expression can be coupled to models of yield and/or temperature altering effects to infer their burn-averaged impact on an implosion. The second-order expression shows significant improvement over lower-order expressions in synthetic data studies. Demonstrations of its applications to synthetic data coupled with models of ion thermal decoupling and radial flows are presented. In the case of thermal decoupling, both first and second-order expressions show reasonable levels of accuracy. To consistently infer the amplitude of radial flow with a <10% error, the second-order equation is required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗