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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗
DOE OSTI2023

Data-Driven Smoothers for Extreme-Scale Computing

Patch-based relaxation refers to a family of methods for solving linear systems which partitions the matrix into smaller pieces often corresponding to groups of adjacent degrees of freedom residing within patches of the computational domain. The two most common families of patch-based methods are block-Jacobi and Schwarz methods, where the former typically corresponds to non-overlapping domains and the later implies some overlap. We focus on cases where each patch consists of the degrees of freedom on a finite element method mesh cell. Patch methods often capture complex local physics much more effectively than simpler point-smoothers such as Jacobi; however, forming, inverting, and applying each patch can be prohibitively expensive in terms of both storage and computation time. To this end, we propose several approaches for performing analysis on these patches and constructing a reduced representation. The compression techniques rely on either matrix norm comparisons or unsupervised learning via a clustering approach. We illustrate how it is frequently possible to retain/factor less than 5% of all patches and still develop a method that converges only a little slower than when all patches are stored/factored.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optimising the processing and storage of visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Techniques: interferometric↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Measurement of mix at the fuel–ablator interface in indirectly driven capsule implosions on the National Ignition Facility

The interface between the capsule ablator and fuel ice layer is susceptible to hydrodynamic instabilities. The subsequent mixing of hot ablator material into the ice reduces fuel compression at stagnation and is a candidate for reduced capsule performance. The ability to diagnose ice–ablator mix is critical to understanding and improving stability at this interface. Combining the crystal backlighter imager with the single line of sight camera on the National Ignition Facility (NIF) allows direct measurement of ice–ablator mix by providing multiple quasi-monochromatic radiographs of layered capsule implosions per experiment with high spatial (∼12 μm) and temporal (∼35 ps) resolution. The narrow bandwidth of this diagnostic platform allows radiography of the inner edge of the capsule limb close to stagnation without capsule self-emission contaminating the data and removes opacity uncertainties typically associated with the spectral content of the radiograph. Analysis of radiographic data via a parameterized forward-fitting Abel inversion technique provides measurements of the distribution of mix mass inwards from the ice–ablator interface. The sensitivity of this mix measurement technique was demonstrated by applying it to layered experiments in which the stability of the ice–ablator interface was expected to vary significantly. Additional experiments suggest that high-density carbon capsules that employ a buried-layer dopant profile suffer from mixing at the innermost doped–undoped interface. Data from these experiments suggest that opacity models used in hydrodynamic simulations of NIF experiments can potentially over-predict the opacity of doped capsules. LLNL-JRNL-850535-DRAFT.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Anomalous Behavior of 2D Janus Excitonic Layers under Extreme Pressures

Newly discovered 2D Janus transition metal dichalcogenides layers have gained much attention from a theory perspective owing to their unique atomic structure and exotic materials properties, but little to no experimental data are available on these materials. Here, experimental and theoretical studies establish the vibrational and optical behavior of 2D Janus S–W–Se and S–Mo–Se monolayers under high pressures for the first time. Chemical vapor deposition (CVD)-grown classical transition metal dichalcogenides (TMD) monolayers are first transferred onto van der Waals (vdW) mica substrates and converted to 2D Janus sheets by surface plasma technique, and then integrated into a 500 µm size diamond anvil cell for high-pressure studies. Overall, the results show that 2D Janus layers do not undergo phase transition up to 15 GPa, and in this pressure regime, their vibrational modes exhibit a nonmonotonic response to the applied pressures (dω/dP). Interestingly, these 2D Janus monolayers exhibit unique blueshift in photoluminescence (PL) upon compression, which is in contrast to many other traditional semiconductor materials. Overall theoretical simulations offer in-depth insights and reveal that the overall optical response is a result of competition between the ab-plane (blueshift) and c-axis (redshift) compression. The overall findings shed the very first light on how 2D Janus monolayers respond under extreme pressures and expand the fundamental understanding of these materials.

2D Janus monolayers↗
DOE OSTI2026

CORE-BFS: Communication-Optimized REctangular-partitioned BFS Achieving 160.845 TeraTEPS on Frontier Supercomputer

Distributed Breadth-First Search (BFS) is fundamental to many large-scale graph applications, but its performance on parallel systems is often limited by high communication overhead. This paper presents CORE-BFS, an extremely scalable GPU-based BFS implementation that introduces a unique rectangular 2D partitioning-based design for Frontier supercomputer. To further improve performance, we propose four key optimizations: (1) Rectangular 2D-partition specific data formats that use two compressed row and one compressed column status array bitmaps combined with a Double Compressed Sparse Row (DCSR) format per partition, reducing memory footprint and inter-rank traffic; (2) Adaptive frontier & communication strategy that unifies top-down and bottom-up traversal on the rectangular layout, uses lazy synchronization in top-down levels, and switches variants based on frontier size to minimize communication overhead; (3) Frontier-split degree-aware update that maps frontier vertices to thread-centric, wavefront-centric, and block-centric kernels based on their degree to improve GPU utilization and memory coalescing; (4) Row-reduction pipeline that overlaps bottom-up adjacency list processing with row-wise bitmap reduction to hide inter-rank latency. Together, these techniques increase parallelism while reducing memory and communication overhead. On the Graph500 benchmark, CORE - BFS scales up to 9,248 Frontier nodes with scale-42 graphs and reaches 160.845 TTEPS, delivering a 5.42 × speedup over our previous Frontier implementation.

Yang, Haoshen [Rutgers University]↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Novel Rotating Flexure-Test Technique for Brittle Materials with Circular Geometries

The four-point bend test is one of the simplest and often the preferred flexural strength evaluation method for brittle materials. In this loading mode, fracture often initiates from a critical surface (or subsurface) flaw when subjected to a tensile stress state. However, if the critical flaw exists on the compression side of the test specimen, it may not activate to grow a crack and hence the resulting flexural strength will be higher than the true value. The goal of this study is to measure the true flexural strength of a solid or hollow cylindrical brittle specimen by ensuring that failure occurs at its weakest point by rotating along its longitudinal axis, thereby exposing and activating its critical surface flaw during a four-point bend test. A novel test fixture has been designed and fabricated, and the true flexural strengths of cylindrical brittle and quasi-brittle tubular specimens have been measured and compared to existing experimental data obtained through traditional four-point bend tests. In this work, experimental results showcase the orientation dependance on flexural strength for various materials. Additionally, similarities between experimental findings and those available in literature, including observations of fracture surfaces and relationships between surface roughness and material strength, are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗
DOE OSTI2023

Digital Twins for Nuclear Power Plants and Facilities

The nuclear digital twin (DT) is the virtual representation of a nuclear energy system across its lifecycle. The nuclear DT uses real-time information and other data sources to improve the process of design, licensing, construction, security, O&M, decommissioning, and waste disposal. By leveraging the knowledge base and experience from the past 40 years of LWR operation, the nuclear DT is helping to accelerate the development and deployment of advanced nuclear technology in areas of passive safety, new fuel forms, instrumentation, and reactor control. For the currently operating nuclear fleet, DTs are reducing the operational risks, increasing plant availability, increasing energy capability, and reducing electricity production costs. For advanced fission and fusion reactors, DTs are being used to design for passive safety and built-in security-by-design. Rapidly deployable small modular reactor (SMR) and microreactor designs compatible with modular construction techniques and advanced manufacturing will be the new normal, reducing the need for large capital expenditures and compressing construction schedules. In addition, lower operational and maintenance costs will be realized by reducing the complexity of operations, staffing needs, and maintenance-related activities.

Kropaczek, Dave↗
DOE OSTI2022

Ta compressibility to 20+ Mbar

Developing techniques for experimentally constraining equation of state (EOS) models for important programmatic materials under extreme conditions is vital for advancing our modeling and predictive capabilities. Tantalum is a frequently used standard material for both calibration and testing, and this report describes our work to both measure Ta compressibility to very high pressures and densities using the Ramp Compression Equation of State platform on the National Ignition Facility (NIF). Using a series of seven shots, increasing in peak pressure with each shot, we have made absolute measurements of the compressibility of Ta along the ramp compression path up to 2.3 TPa. Previous experimental measurements constrained the cold compressibility up to ~400 GPa, and there is a spread in the EOS models above that pressure. To improve communication and collaboration between the experimental team and the EOS development group, these new data, along with other experimental and theoretical constraints, were used to develop a Ta equation of state table, M73000. This report first describes the experimental measurements made at the NIF, and then discusses the construction of the EOS model. The ramp compression data can be found in tabular form in Appendix A. Appendix B contains some additional experimental details. In Appendix C we include some additional details on the EOS modeling. At the end of this document, we have attached a detailed report on the NIF ramp compression platform itself and in the main text referenced specific sections for additional details.

36 MATERIALS SCIENCE↗
DOE OSTI2023

EGS Collab Experiment 2: Hydraulic Pressure Test Results

The EGS Collab experiment 2 was focused on testing shear stimulation techniques. Shear stimulation, in this case, means using hydraulic pressure to cause shear slip on preexisting fracture or fault planes such that the hydraulic conductivity of the fracture or fault increases. The concept is to create a percolating network of permeable fractures by enhancing the permeability of a primarily preexisting network of fractures. To test this concept the hydraulic pressures for experiment 2 were kept below the the estimated magnitude of the least compressive principal stress based on a set of stress measurements in nearby well TV4100. All tests for experiment 2 were performed by applying hydraulic pressure to well E2-TC. Subsequent stimulations in both E2-TC and E2-TU where the injection pressure was increased above the least compressive principal stress are considered part of experiment 3, which is documented separately. The data are organized in directories labeled by the depth range isolated between packers.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

X-ray phase contrast and absorption imaging for the quantification of transient cavitation in high-speed nozzle flows

In this work, high-flux synchrotron radiation has been employed in a time-resolved manner to characterise the distinct topology features and dynamics of different cavitation regimes arising in a throttle orifice with an abrupt flow-entry contraction. Radiographs obtained though both X-ray phase- contrast and absorption imaging have been captured at 67,890 frames per second. The flow lied in the turbulent regime (Re=35,500), while moderate (CN=2.0) to well-established (CN=6.0) cavitation conditions were examined encompassing the cloud and vortical cavitation regimes with pertinent transient features, such as cloud-cavity shedding. X-ray Phase-Contrast Imaging (XPCI), exploiting the shift in the X-ray wave phase during interactions with matter, offers sharp-refractive index gradients in the interface region. Hence, it is suitable for capturing fine morphological fluctuations of transient cavitation structures. Nevertheless, the technique cannot provide information on the quantity of vapour within the orifice. Such data have been obtained utilising absorption imaging, where beam attenuation is not associated with scattering and refraction events, and hence can be explicitly correlated with the projected vapour thickness in line-of-sight measurements. A combination of the two methods is proposed, as it has been found is capable of quantifying the vapour content arising in the complex nozzle flow, while also faithfully illustrating the dynamics of the highly-transient cavitation features.

42 ENGINEERING↗
DOE OSTI2021

Performance and Durability of Pure-Water-Fed Anion Exchange Membrane Electrolyzers Using Baseline Materials and Operation

Water electrolysis powered by renewable electricity produces green hydrogen and oxygen gas, which can be used for energy, fertilizer, and industrial applications and thus displace fossil fuels. Pure-water anion-exchange-membrane (AEM) electrolyzers in principle offer the advantages of commercialized proton-exchange-membrane systems (high current density, low cross over, output gas compression, etc.) while enabling the use of less-expensive steel components and nonprecious metal catalysts. AEM electrolyzer research and development, however, has been limited by the lack of broadly accessible materials that provide consistent cell performance, making it difficult to compare results across studies. Further, even when the same materials are used, different pretreatments and electrochemical analysis techniques can produce different results. Here, we report an AEM electrolyzer comprising commercially available catalysts, membrane, ionomer, and gas-diffusion layers operating near 1.9 V at 1 A cm –2 in pure water. After the initial break in, the performance degraded by 0.67 mV h –1 at 0.5 A cm –2 at 55 °C. We detail the key preparation, assembly, and operation techniques employed and show further performance improvements using advanced materials as a proof-of-concept for future AEM-electrolyzer development. Here, the data thus provide an easily reproducible and comparatively high-performance baseline that can be used by other laboratories to calibrate the performance of improved cell components, nonprecious metal oxygen evolution, and hydrogen evolution catalysts and learn how to mitigate degradation pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Modeling of a spatially resolved ion temperature diagnostic for inertial confinement fusion

Here, the performance of modern laser-driven inertial confinement fusion (ICF) experiments is degraded by contamination of the deuterium–tritium (DT) fuel with high-Z material during compression. Simulations suggest that this mix can be described by the ion temperature distribution of the implosion, given that such contaminants deviate in temperature from the surrounding DT plasma. However, existing neutron time-of-flight (nTOF) diagnostics only measure the spatially integrated ion temperature. This paper describes the techniques and forward modeling used to develop a novel diagnostic imaging system to measure the spatially resolved ion temperature of an ICF implosion for the first time. The technique combines methods in neutron imaging and nTOF diagnostics to measure the ion temperature along one spatial dimension at yields currently achievable on the OMEGA laser. A detailed forward model of the source and imaging system was developed to guide instrument design. The model leverages neutron imaging reconstruction algorithms, radiation hydrodynamics and Monte Carlo simulations, optical ray tracing, and more. The results of the forward model agree with the data collected on OMEGA using the completed diagnostic. The analysis of the experimental data is still ongoing and will be discussed in a separate publication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗