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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
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Heat Treatment Optimization of Laser Powder Bed Fusion Additive Manufacture C103

Laser Powder Bed Fusion (L-PBF) and Laser Powder Directed Energy Deposition (LP-DED) additive manufacture C103 is in development for propulsion applications that operate under extreme environments. The standard post-process heat treatment has called for vacuum stress relief, however, depending on the application or certification requirements hot isostatic press (HIP) may be required. Although C103 density responds well to HIP the associated grain growth results in a reduction in mechanical properties. An investigation was conducted to look at the potential benefit of leveraging HIP followed by rapid cooling to minimize grain growth. A series of heat treatment schedules varying temperature and pressure were conducted followed by microstructural and mechanical characterization.

Additive Manufacturing↗
DOE OSTI2023

Unraveling the Mechanics: Computational Modeling of Residual Stresses in U10Mo Fuel with Aluminum Cladding

LEU (Low Enriched Uranium) plate-type fuel elements consist of a high-density, low-enrichment U–Mo alloy-based fuel foil encapsulated in an aluminum cladding and is fabricated through the Hot Isostatic Pressing (HIP) technique. Understanding the mechanism of possible failure modes of nuclear fuel is critical to mitigate potential consequences. One of the major contributing factors in various failure modes is stress and it greatly affects the integrity under temperature, pressure, and irradiation. These stresses could originate from various sources, including manufacturing, and operation, and can lead to deformation, cracking, or even complete failure of fuel elements. This study focuses on the development of a computational model that accurately predicts the residual stresses generated in the U10Mo fuel during fabrication. It has been observed that cladding creep has a substantial impact on the residual stresses in the U10Mo fuel post the HIP fabrication process. Furthermore, during the HIP bonding process the fuel plate system is heated to a temperature of 560 oC and as a result, the aluminum cladding transitions from (Al 6061-T6 to -O). This presents a challenge in capturing the change in material properties accurately. Therefore, a temperature dependent creep model such as hyperbolic sine creep model is considered to estimate the creep properties of the Aluminum cladding. The proposed calibrated creep model accurately predicts the residual stresses in the U10Mo fuel foil and agrees well with the experiments.

42 ENGINEERING↗
DOE OSTI2025

Evaluation of U10Mo Fuel Plate Performance Modeling Over Hot Isostatic Press and Hydraulic Bending for MURR DDE Plates

The United States High Performance Research Reactor Program’s objective is to reduce the amount of highly enriched uranium currently implemented in research reactors. The conversion of these research reactors requires designing a monolithic U10Mo plate fuel, with the fuel plate geometry being dependent on each research reactor. The process of forming the plates includes a hot isostatic pressing (HIP) to manufacture a prototypic plate. In the case of the Missouri University Research Reactor (MURR) design demonstration element (DDE) plate manufacture, plates that have been through HIP are then curved using dies and a hydraulic press to impart the desired curvature. Both fabrication processes impart residual stresses into each fuel plate region, with the curvature of the plates taking some regions of the fuel plate up to their material yield stresses, accompanied by plastic strain. The amount of plastic strain and stress imparted onto each MURR DDE plate is determined by the radius of curvature, thickness of each region, and overall width of the fuel plates. Furthermore, this work aims to predict the yield stresses and strain using ABAQUS to simulate the proposed fabrication process of the MURR DDE plates, accompanied by discussion over the stresses and strains as to their relation to nuclear fuel performance and the impact they will have during early irradiation.

ABAQUS↗
DOE OSTI2020

Microstructural Evolution and ORR Activity of Nanocolumnar Platinum Thin Films with Different Mass Loadings Grown by High Pressure Sputtering

Nanocolumnar platinum thin films (Pt-TFs) with different Pt mass loadings were grown by high pressure sputtering (HIPS) and investigated as oxygen reduction reaction (ORR) electrocatalysts for polymer electrolyte membrane fuel cell applications. Mass loading was controlled by changing the sputter deposition time. A cauliflower-like columnar microstructure was achieved by depositing the Pt-TFs onto a microporous layer (MPL)-like surface composed of carbon particles in order to mimic catalyst-coated gas diffusion electrodes. Microstructural evolution of HIPS Pt-TFs and their ORR activity were investigated. Electrochemical characterization of the nanocolumnar Pt-TFs was performed by cyclic voltammetry and rotating disk electrode measurements on Pt-TF/MPL-like-layer/glassy-carbon samples in an aqueous perchloric acid electrolyte. The electrochemically active surface area increased from 18 to 39 m 2 g –1 as the Pt mass loading was decreased. Specific activity of the films was similar (~600 μA cm –2 ) for all Pt mass loadings, due to the similar nanoparticle sizes of ~5 nm as observed by transmission electron microscopy and X-ray diffraction. Furthermore, mass activity of the films increased from 0.11 to 0.26 A mg –1 as the Pt mass loading was decreased, which is an indication of the effective Pt utilization and better access through the catalyst layer at lower Pt mass loadings.

08 HYDROGEN↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Studying performance portability of LAMMPS across diverse GPU-based platforms

The molecular dynamics simulation software, LAMMPS, utilizes the Kokkos acceleration library to port computation to a diverse set of architectures including those based on GPU accelerators. In addition to Kokkos, LAMMPS contains a vast code base that leverages the CUDA application programming interface using library functions such as cuFFT, CUDA's fast-fourier transform (FFT) library, and, more recently, also support for AMD's Heterogeneous Interface for Portability (HIP) that is rapidly growing. While preparing LAMMPS tests for the AMD GPU-based test system precursors to Frontier, we investigated several strategies for accelerating LAMMPS on AMD GPUs, using the AMD Instinct MI100 and MI250X. In this work, we integrated the HIP FFT library, hipFFT, into the particle-particle particle-mesh (PPPM) long-range solver, which allowed the porting of PPPM calculations to the GPUs. Kokkos behavior on the MI100 and MI250X was also investigated through the package kokkos command of LAMMPS, targeting communication, memory usage, and particle grid decomposition. The Tersoff, Reax, Lennard-Jones (LJ), EAM, Granular, and PPPM potentials were investigated in this effort, and results from these experiments are provided. In conclusion, the selected potentials were run on Spock (AMD Instinct MI100), Crusher (AMD Instinct MI250X), AFW HPC11 (NVIDIA A100) and Summit (NVIDIA V100), for comparison. Operational roofline models were constructed and analyzed for the Tersoff, Reax, and Lennard–Jones potentials on Crusher and Summit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Large language model evaluation for high–performance computing software development

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. Finally, we expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A multi-backend autotuning study of feature selection on GPUs

Abstract Feature selection is an important step in machine learning that can benefit from GPU acceleration. As the number of GPU vendors increases, it is imperative to adapt algorithms such as the minimum Redundancy Maximum Relevance (mRMR) feature selection method to different backends that support several GPU architectures. This work presents a multi-backend implementation of mRMR across CUDA, HIP, and SYCL, and studies its performance when combined with Bayesian optimization and transfer learning to automatically tune execution parameters for different platforms and datasets. Our experimental results show that when tuned, CUDA and HIP achieve comparable performance on NVIDIA architectures, while SYCL exhibits a moderate performance gap. Overall, this work highlights the impact of backend choice and autotuning on GPU-accelerated feature selection and provides insights into deploying mRMR across heterogeneous environments.

Beceiro, Bieito (ORCID:0000000333014890)↗
DOE OSTI2024

Characterization and Rationalization of Microstructural Evolution in GRCop-84 Processed by Laser-Powder Bed Fusion (L-PBF)

In this study, prismatic geometries of GRCop-84 [Cu-8Cr-4Nb (at. pct)] were built with laser-powder bed fusion (L-PBF) process. The samples were sectioned parallel or perpendicular to the build direction and characterized in the as-built and after post-processing with a hot-isostatically pressing (HIP) treatment. The microstructure and phase evolutions were evaluated with optical microscopy, scanning electron microscopy (SEM), electron backscattered diffraction (EBSD), and high-temperature X-ray diffraction (HTXRD) up to 1223 K. The samples in the as-built conditions exhibited predominantly columnar epitaxial and misoriented Cu-FCC grains. The microstructure evolutions are discussed based on locations within the overall build geometry, the dynamics of small melt pool shape and sectioning effects. The above grain structure did not change significantly during post-process HIP treatment. The stability of this FCC grain structure is attributed to the formation of primary stable Cr 2 Nb (Laves phase) during L-PBF, even before the emergence of FCC grains from liquid. The stability of Cr 2 Nb in both as-built and HIPed samples were evaluated using high-temperature X-ray diffraction measurements and compared with that of gas-atomized powder. The significance of these results is discussed with reference to aerospace applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding Oxide–Metal Interactions During Hot Isostatic Pressing to Diffusion Bond Aluminum Alloy 6061 Plates

The interaction between Mg, Si, and Al 2 O 3 during hot isostatic pressing diffusion bonding of aluminum alloy 6061 (AA6061) plates was investigated through thermodynamic calculations and experimental microstructural characterization. Thermodynamic calculations as functions of temperature, pressure, and composition revealed that the interaction among Mg, Si, and Al 2 O 3 yields Mg 2 Si and either MgO + Al or MgAl 2 O 4 + Al, facilitating the reduction of Al 2 O 3 and allowing Al/Al metallic bonds to form. Total pressure variation had a negligible influence on the oxygen partial pressure, and consequently, the reaction product formation. Oxygen partial pressure variation as a function of temperature and initial amount of Al 2 O 3 determined the formation of either MgO or MgAl 2 O 4 . Experimental Hot Isostatic Pressure (HIP) bonding at 723 K and 833 K under a constant pressure of 1017 atm documented the cooling rate-dependent formation of β–Mg 2 Si precipitates. High-resolution transmission electron microscopy imaging and selected area electron diffraction patterns verified the formation of β–Mg 2 Si and MgO at the interface but did not detect MgAl 2 O 4 . In conclusion, findings from this study clarify the role of thermochemical interactions in oxide disruption and bonding mechanisms during HIP diffusion bonding of AA6061 and provide guidance for optimizing joining processes for monolithic nuclear fuel assemblies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Dislocation channel broadening–A new mechanism to improve irradiation-assisted stress corrosion cracking resistance of additively manufactured 316 L stainless steel

Additively manufactured (AM) 316 L stainless steel (SS) after hot isostatic pressing (HIP) was found to exhibit superior resistance to irradiation-assisted stress corrosion cracking (IASCC) in high-temperature water, as compared to wrought 316 L SS. The well-accepted IASCC factors of radiation-induced segregation (RIS) and radiation hardening are not accurate descriptions of IASCC susceptibility in this case. A decreased strain localization along grain boundaries, caused by dislocation channel broadening, was confirmed to suppress crack initiation. A unique distribution of irradiation-induced defects in HIP AM 316 L SS eased dislocation cross-slip compared to those in the wrought counterpart, thus increasing the channel width near the grain boundaries. Here for the first time, this study highlights the importance of dislocation channel broadening as a potential mechanism to further improve the IASCC resistance of 316 L SS and provides direct experimental evidence based on commercial-grade materials.

316L stainless steel↗
DOE OSTI2020

Iodosodalite synthesis with hot isostatic pressing of precursors produced from aqueous and hydrothermal processes

Iodosodalite powders were synthesized using aqueous and hydrothermal methods, with or without sodium borosilicate glass binders, and then hot isostatically pressed (HIP) at 900 ? and 175 MPa for 3 h to convert into waste forms. After HIPing, the structures, compositions, morphologies, porosities, and leach rates of the aqueous and hydrothermally produced iodosodalite samples were compared. X-ray diffraction patterns of HIPed samples showed that iodosodalite remained as the dominant phase. However, the fraction of the iodosodalite phase decreased after HIPing, indicating decomposition of the iodosodalite structure during the HIP process. Scanning electron microscopy and chemical imaging on the cross sections of HIPed samples showed homogeneous elemental distribution for aqueous-grown iodosodalite, whereas hydrothermally grown iodosodalite samples had more heterogeneity and porosity. The densities of HIPed samples with more glass binder were generally higher than sampled HIPed without binder. The leach tests on samples containing 20 mass% glass binder showed that the iodine dissolution rate of HIPed hydrothermally grown iodosodalite was about 4 times higher than iodosodalite by aqueous method. This study provides alternative techniques for immobilizing iodine-streams in iodosodalite waste forms.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Influence of neutron irradiation on Al-6061 alloy produced via ultrasonic additive manufacturing

Samples of aluminum alloy 6061 produced via ultrasonic additive manufacturing (UAM) were irradiated in the High Flux Isotope Reactor (HFIR) up to 17.3 dpa at ~70°C while in contact with water using perforated rabbit capsules. The irradiation campaign included as-received (AR) material, specimens subjected to various post-weld heat treatments (PWHTs, including hot isostatic pressing [HIP]), and reference (wrought) alloy samples. Mechanical tensile tests, accompanied by digital image correlation (DIC) analysis, fractography, and metallography, were performed as a part of the post-irradiation evaluation. The X- and Y-specimens (i.e., oriented in the sonotrode moving and vibration directions, respectively) showed pronounced radiation hardening and ductility decrease. Specific serration flow behavior and propagation of deformation bands were observed under various material conditions up to 3.5 dpa but disappeared at 17.3 dpa. In all cases, the fracture mechanism of X- and Y-specimens was ductile; ductile dimples dominated the fracture surface. Irradiated X- and Y-specimens showed good performance, regardless of material conditions (AR or PWHT). The performance of Z-specimens oriented in the build direction was strongly dependent on the PWHT. The AR and aged specimens showed fracture stress decrease with dose, and they experienced fracture under irradiation after 3.5 dpa; specimen cross section analysis revealed specific interface degradation that was likely related to corrosion. Recrystallization significantly improved in-reactor performance. Finally, HIP suppressed interface degradation due to recrystallization and pore removal, which led to good in-reactor performance for Z-specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of orientation-dependent deformation mechanisms in additively manufactured Zr using in-situ micromechanical testing: Twinning and orientation gradient

Here, orientation-dependent plasticity in zirconium (Zr)-alloy, which is produced through ultrasonic additive manufacturing (UAM) with subsequent hot-isostatic pressing (HIP), was analyzed by recording electron backscattered diffraction (EBSD) data during in-situ micromechanical testing. Three (0001) plane orientations of a hexagonal close-packed (HCP) structure were analyzed parallel to (1) the rolling direction [X||tensile direction (TD)], (2) the build direction [Z], and (3) the transverse direction [Y]. The analysis revealed that the grains with ~<0001>||TD show twin-dominant plasticity with three variants from {10 $\overline{1}$ 2}<$\overline{1}$ 011>; minor slipping with ($10\overline{1}$ 1)[$\overline{2}$ 113], and ($11\overline{2}$ 2)[$\overline{1} \overline{1}$ 23] pyramidal slip has also been observed. However, grains with orientation ~<0001>⊥TD are mainly sensitive to dislocations assisted plasticity-leading to orientation gradients formation. Furthermore, the neck formation was identified as originating from higher populated micro-crack locations and their association with localized plasticity at defect points in the UAM material. These results demonstrate that minimization of impurities to enable grain growth across prior foil interfaces makes HIP an effective methodology for producing Zr plate, with deformation characteristics expected for conventionally manufactured Zr.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural heterogeneities in additively manufactured refractory alloy C103 and their implications for room and elevated temperature mechanical behavior

Considering the vast component design space enabled by fusion-based additive manufacturing (F-BAM) processes, e.g., directed energy deposition (DED), the scale-up manufacturing of Nb-alloys with F-BAM is advantageous for structural applications. However, varying thermokinetic parameters-induced microstructural heterogeneities are prevalent within the F-BAM processed alloys. Such microstructural heterogeneities can have significant implications for the room and elevated temperature mechanical behavior. While a few studies investigating F-BAM processed alloy C103 are available, none of these studies investigate the microstructural heterogeneities – including those associated with solidification growth modes and second phase particles – and the effect thereof on the mechanical behavior. To this end, we investigate the microstructurally heterogeneous regions with varying solidification growth morphologies, segregation behavior, and second phase particle attributes within the laser-DED processed alloy C103. The implications of such heterogeneous regions for room- and elevated-temperature tensile behavior and damage mechanisms are revealed. Particularly, the interface between the cellular and planar region is identified as susceptible to deformation localization. The implications of hot isostatic pressing (HIP) for the consolidation behavior, microstructural evolution, and resulting mechanical behavior are also discussed. Although the recrystallization and grain growth led to a reduced yield strength in the HIPed condition, the homogenization of microstructure alleviated the deformation localization sites, such as the planar/cellular interface within the melt pool. The homogenized microstructure alongside the enhanced consolidation upon HIP led to an enhanced elongation to failure. Findings establish microstructural design considerations in F-BAM processed Nb alloys and also facilitate design of post-processing heat treatments for achieving improved mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Micro X-ray computed tomography examination of mini plate fuel with hot isostatic pressed aluminum cladding

In order to minimize proliferation risks and improve security of nuclear material, the United States high performance research and test reactors (USHPRR) program is tasked with converting nuclear reactors that are fueled with highly enriched uranium (HEU) fuels to operate with low-enriched uranium (LEU) nuclear fuels. One favorable LEU fuel configuration is plate fuel with a metallic uranium-molybdenum foil clad within an aluminum alloy (AA 6061). In this fuel, the aluminum cladding is bonded with a hot isostatic pressing (HIP) method to seal the cladding around the fuel meat. However, the HIP process parameters influence the cladding performance, as a defective or incomplete bond can cause a pathway for corrosion. Micro X-ray computed tomography (XCT), a nondestructive technique that provides volumetric imaging, can be applied to inspect fuel plate cladding at the engineering scale. In this work, XCT methodology was developed and successfully utilized to not only observe the bond line of unirradiated mini fuel plates, but to also identify subsurface abnormalities in the plates’ cladding. Importantly, in future work, this technique could be applied to fuel plates, pre- and post-irradiation, to quantify irradiation effects on cladding defects and bond line integrity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry

Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗