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DOE OSTI2021

A novel energy balance approach for a verifiable and accurate solution of radiation extinction in purely absorbing particle clouds

Here we consider the problem of radiation transport through purely absorbing particle clouds. The gold-standard solution of particle-resolved Monte Carlo ray-tracing method to this problem is computationally expensive and therefore solving the radiation transport equation (specifically, the Beer-Bouguer law (BB-law)) on a Eulerian mesh is often preferred. While the absorption coefficient in the real problem is infinite, the BB-law approximates it to be a finite number in the form of number density through a set of assumptions. For particle clouds that do not obey these assumptions, the BB-law predicts an incorrect exponential decay. Also, when the number density is computed using the nearest-neighbor approach, the BB-law solution diverges when the Eulerian mesh size becomes closer to or smaller than the particle size. This numerical divergence is due to the homogenization error. Although the filtering strategy for number density minimizes the homogenization error, it still converges to an incorrect exponential decay for particle clouds that break the BB-law constraints and the cost of the filtering is equivalent to that of the gold-standard solution. In this study, we develop a novel, highly accurate, verifiable and cost-effective solution to the radiation transport equation on a Eulerian domain using an energy balance approach where we derive an expression for the absorption coefficient as a function of particle and Eulerian mesh sizes. We apply our new method to Poisson and turbulent particle clouds that violate all the BB-law constraints and show that the solution is converging upon the mesh refinement, and eventually, we recover the same gold-standard solution for a much cheaper computational cost.

42 ENGINEERING↗

DOE OSTI2026

Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to Ba⁡(Co,Fe,Zr,Y)⁢O 3−𝛿 combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

A Full-Featured User Friendly CO 2 -EOR and Sequestration Planning Software

A Full-Featured, User Friendly CO 2 -EOR and Sequestration Planning Software This project addressed the development of an integrated software solution that includes a graphical user interface, numerical simulation, visualization tools and optimization processes for reservoir simulation modeling of CO 2 -EOR. The objective was to assist the industry in the development of domestic energy resources by expanding the application of CO 2 -EOR technologies, and ultimately to maximize the CO 2 } sequestration capacity of the U.S. The software resulted in a field-ready application for the industry to address the current CO 2 -EOR technologies. The software has been made available to the public without restrictions and with user friendly operating documentation and tutorials. The software (executable only) can be downloaded from NITEC’s website at www.nitecllc.com. This integrated solution enables the design, optimization and operation of CO 2 -EOR processes for small and mid-sized operators, who currently cannot afford the expensive, time intensive solutions that the major oil companies enjoy. Based on one estimate, small oil fields comprise 30% of the of total economic resource potential for the application of CO 2 -EOR processes in the U.S. This corresponds to 21.7 billion barrels of incremental, technically recoverable oil using the current “best practices”, and 31.9 billion barrels using “next-generation” CO 2 -EOR techniques. The project included a Case Study of a prospective CO 2 -EOR candidate field in Wyoming by a small independent, Linc Energy Petroleum Wyoming, Inc. NITEC LLC has an established track record of developing innovative and user friendly software. The Principle Investigator is an experienced manager and engineer with expertise in software development, numerical techniques, and GUI applications. Unique, presently-proprietary NITEC technologies have been integrated into this application to further its ease of use and technical functionality.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Making many-body interactions nearly pairwise additive: The polarized many-body expansion approach

The Many-Body Expansion (MBE) is a useful tool to simulate condensed phase chemical systems, often avoiding the steep computational cost of usual electronic structure methods. However, it often requires higher than 2-body terms to achieve quantitative accuracy. In this work, we propose the Polarized MBE (PolBE) method where each MBE energy contribution is treated as an embedding problem. In each energy term, a smaller fragment is embedded into a larger, polarized environment and only a small region is treated at the high-level of theory using embedded mean-field theory. The role of polarized environment was found to be crucial in providing quantitative accuracy at the 2-body level. PolBE accurately predicts noncovalent interaction energies for a number of systems, including CO 2 , water, and hydrated ion clusters, with a variety of interaction mechanisms, from weak dispersion to strong electrostatics considered in this work. We further demonstrate that the PolBE interaction energy is predominantly pairwise unlike the usual vacuum MBE that requires higher-order terms to achieve similar accuracy. We numerically show that PolBE often performs better than other widely used embedded MBE methods such as the electrostatically embedded MBE. Finally,owing to the lack of expensive diagonalization of Fock matrices and its embarrassingly parallel nature, PolBE is a promising way to access condensed phase systems with hybrid density functionals that are difficult to treat with currently available methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Does DFT+U mimic hybrid density functionals?

Here, this work examines the question of how a Hubbard U correction to a local exchange–correlation functional compares with adding Hartree–Fock exchange to a local functional for both solid-state and molecular properties. We compute a solid-state property, namely the band gap, and thermochemical molecular properties, in particular main-group bond energies, transition-metal–ligand bond energies, and barrier heights, to elucidate whether the DFT+U method mimics hybrid DFT. We find that a calculation with a Hubbard U correction may or may not mimic a hybrid functional – depending on the atom, the subshell, and the property to which it is applied. For band gaps, we find that adding a Hubbard U correction to the valence d orbitals of transition metals increases the band gap, which thereby gets closer to the experimental value, while adding a Hubbard U correction to valence s or p orbitals of main group elements need not always increase the band gap. For molecular thermochemistry, we find that adding a Hubbard U correction to a local density functional need not have the same effect as adding Hartree–Fock exchange to a local density functional. For example when compared to a local DFT, hybrid DFT increases the barrier height in all cases, but DFT+U does not always increase the barrier height. For the band gaps of transition metal monoxides, the Hubbard-corrected results lowered the mean errors significantly and were comparable to what could be achieved with a much more expensive hybrid functional, but for reaction barrier heights and bond energies the Hubbard correction can lower the mean error by approximately a kcal/mol. As part of the analysis, we also compare VASP and Gaussian 09 calculations for the same density functional.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Keras2c: A library for converting Keras neural networks to real-time compatible C

With the growth of machine learning models and neural networks in measurement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time consuming work to integrate into existing code bases, or only support a very limited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural network models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent layers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ~ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Prediction of impact sensitivity, heat of formation and heat of explosion using atomic connectivity

In these proceedings we revisit a large collection of explosives and explosive descriptors with the goal of predicting impact sensitivity using only local atomic environments that can be deciphered from molecular SMILES strings as descriptors without utilizing empirically measured values or computationally expensive electronic structure calculations. From the original database of nearly 500 descriptors, removing empirically measured and electronic structure values decreased the number of descriptors to 135, which we reduced to 18 the most important descriptors using Random Forests. The condensed model predicted impact sensitivity with essentially the same accuracy as the existing, more complex model (R 2 = 0.788 and RSME = 0.312), while remaining applicable to all types of explosives (Peroxides, azides, C-Nitros, Nitroamines, Nitrate Esters, etc.). In addition to impact sensitivity, we proposed similar models to accurately predict values heat of formation (ΔH f ) and heat of explosion (Q), with R 2 = 0.966 and 0.916, respectively. In conclusion, the work in these proceedings allows for prediction of explosive performance and sensitivity with only chemical structure information and an estimate of density.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

A Unit Testing Framework for Scientific Legacy Code

Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure's performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

High resolution viscous fingering simulation in miscible displacement using a p-adaptive discontinuous Galerkin method with algebraic multigrid preconditioner

High resolution simulation of viscous fingering can offer an accurate and detailed prediction for subsurface engineering processes involving fingering phenomena. The fully implicit discontinuous Galerkin (DG) method has been shown to be an accurate and stable method to model viscous fingering with high Peclet number and mobility ratio. In this study, we present two techniques to speedup large scale simulations of this kind. The first technique relies on a simple p-adaptive scheme in which high order basis functions are employed only in elements near the finger fronts where the concentration has a sharp change. As a result, the number of degrees of freedom is significantly reduced and the simulation yields almost identical results to the more expensive simulation with uniform high order elements throughout the mesh. The second technique for speedup involves improving the solver efficiency. We present an algebraic multigrid (AMG) preconditioner which allows the DG matrix to leverage the robust AMG preconditioner designed for the continuous Galerkin (CG) finite element method. The resulting preconditioner works effectively for fixed order DG as well as p-adaptive DG problems. Finally, with the improvements provided by the p-adaptivity and AMG preconditioning, we can perform high resolution three-dimensional viscous fingering simulations required for miscible displacement with high Peclet number and mobility ratio in greater detail than before for well injection problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Multi-Fidelity Scheme for Accelerating 4D Finite Element Analysis for Mircoreactors

Next-generation microreactors are currently being designed to be operated terrestrial and extraterrestrial for remote surface power production. These systems will provide an alternative source of carbon-free energy that is versatile and can be utilized for various applications. These applications of microreactors have prompted the usage of high-fidelity unstructured finite element (FE) based approaches to provide time-dependent solutions for multiple physics fields. Computing these high-fidelity (4-D) solutions for multiple design iterations, physics fields, and transient events requires an immense computational cost. These high-fidelity solutions are computational expensive and the cost can be reduced through the implementation of less accurate low-fidelity solutions. Unlike the current fleet of commercial nuclear reactors, these next-gen systems present challenges due to the material and physical limitations required. Due to these constraints, the brute force technique of parameterizing important system characteristics determines whether a design meets the project objective. A system such as a nuclear reactor could have thousands of design parameters that affect system performance. In order to analyze the entire parameter space of such a complex system would require millions of CPU hours and countless design iterations. This costly approach is not practical due to regulatory and budget limitations. In this proposal, an approach that utilizes hybrid high-fidelity and low-fidelity FE models to reduce the computational cost of evaluating these applications in 4-D will be presented. The accuracy of the high-fidelity model and the computational efficiency of the low-fidelity model are taken advantage of to produce a solution that closely resembles the full order high-fidelity solution. By utilizing an unconverged coarse FE mesh, operation limits such as temperature, structural loading, etc., can be evaluated in an accelerated fashion and then can be spatially interpolated onto a finer mesh. The resulting error arising from the coarse mesh can be mitigated with a discrepancy function that actively quantifies and corrects the error in the coarse solution. This discrepancy function can be periodically updated with high-fidelity calculations across the temporal domain thus requiring less iterations on the fine mesh. As a result, the computational cost can be reduced for the evaluation and design iteration of microreactors. The proposal for this research contains three sections: Section 2 provides a literature review, Section 3 outlines the methodology for the proposed multi-fidelity scheme, and Section 4 displays preliminary results of the proposed research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗

DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗

DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2015

SRAM-Based Digital Arbiter PUF

The increased use of Field Programmable Gate Arrays (FPGAs) in critical systems brings new challenges in securing the diversely programmable fabric from cyber-attacks. FPGAs are an inexpensive, efficient, and flexible alternative to Application Specific Integrated Circuits (ASICs), which are becoming increasingly expensive and impractical for low volume manufacturing as technology nodes continue to shrink. Unfortunately, FPGAs are not designed for high security applications, and their high-flexibility lends itself to low security and vulnerability to malicious attacks. Similar to securing an ASIC’s functionality, FPGA programmers can exploit the inherent randomness introduced into hardware structures during fabrication for security applications. Physically Unclonable Functions (PUFs) are one such solution that uses the die specific variability in hardware fabrication for both secret key generation and verification. PUFs strive to be random, unique, and reliable. Throughout recent years many PUF structures have been presented to try and maximize these three design constraints, reliability being the most difficult of the three to achieve. This thesis presents a new PUF structure that combines two elementary PUF concepts (a bi-stable SRAM PUF and a delay-based arbiter PUF) to create a PUF with increased reliability, while maintaining both random and unique qualities. Properties of the new PUF will be discussed as well as the various design modifications that can be made to tweak the desired performance and overhead.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Machine Learning Prediction of H Adsorption Energies on Ag Alloys

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. This has prompted a significant amount of work towards efficiently predicting adsorption energies without performing expensive quantum chemical calculations, based on semi-empirical or data scientific models. Here, we develop machine learning models to predict H adsorption energies on stepped Ag surface alloys using a database of energies obtained from first-principles electronic structure calculations based on density functional theory. Starting with a simple model and iteratively refining the features extracted from the calculations, we determine the key characteristics of successful models, and reduce the number of features required for accurate prediction of the adsorption energy. We find that nonlinear models are essential for accurate predictions. Moreover, detailed comparison of model predictions and calculations reveal a counterintuitive "selfpassivation" induced by some alloying elements, as well as unexpected discontinuous electronic structure effects. These findings are rationalized using tight-binding model hamiltonians and by analyzing the electronic density of states. Lastly, we show that we can estimate the reliability of a particular prediction, mitigating the effects of errors in the small fraction of cases where these errors are significant.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Optimization of Geometric Perturbations on a Rod Moving Through a High Explosive Target

After completing a study to ensure the simulation results were converged, several high resolution 3D Smoothed Particle Hydrodynamic (SPH) simulations of copper rods impacting a high explosive (LX14) target were performed. This was then formulated into an optimization problem: I wanted to find the optimum shape and location of a perturbation on the rod that would maximize its erosion after it left the target. The shape of the perturbation was modeled as a 2D Gaussian bump and parameterized by its location along the rod axis (z 0 ) and amplitude (A). The final mass of the coherent part of the rod as it leaves the target was used as a metric to represent the erosion of the rod, and the optimization was formulated to maximize this metric with respect to the aforementioned design variables. Due to the expensive nature of the high-fidelity 3D SPH simulations, a surrogate model needed to be chosen so that many function calls to the optimizer would be feasible. Thus, a strategic full factorial sampling plan was chosen to build a dataset, which consisted of 24 high-fidelity simulations. Two surrogate models, a third order polynomial regression model and a Gaussian Process Model, were analyzed using a 14%/86% test/train holdout technique. The root mean square and R2 score of the test set was used to determine the best model, and the third order polynomial regression model was chosen as the surrogate model. Finally, the Nelder-Mead Simplex and Basin-hopping optimization algorithms were implemented, and it was found that the two algorithms gave slightly different optimum values. Nelder-Mead gave an optimum point of [z* 0 ;A*] = [9:9;0:4] and Basin-Hopping gave an optimum value of x* = [z* 0 ;A*] = [9:2;0:1].

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗