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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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15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Global Simulations of Phase State and Equilibration Time Scales of Secondary Organic Aerosols with GEOS-Chem

The phase state of secondary organic aerosols (SOA) can range from liquid through amorphous semisolid to glassy solid, which is important to consider as it influences various multiphase processes including SOA formation and partitioning, multiphase chemistry, and cloud activation. In this study, we simulate the glass transition temperature and viscosity of SOA over the globe using the global chemical transport model, GEOS-Chem. The simulated spatial distributions show that SOA at the surface exist as liquid over equatorial regions and oceans, semisolid in the midlatitude continental regions, and glassy solid over lands with low relative humidity. The predicted SOA viscosities are mostly consistent with the available measurements. In the free troposphere, SOA particles are mostly predicted to be semisolid at 850 hPa and glassy solid at 500 hPa, except over tropical regions including Amazonia, where SOA are predicted to be low viscous. Phase state also exhibits seasonal variation with a higher frequency of semisolid and solid particles in winter compared to warmer seasons. We calculate equilibration time scales of SOA partitioning (τ eq ) and effective mass accommodation coefficient (α eff ), indicating that τ eq is shorter than the chemical time step of GEOS-Chem of 20 min and αeff is close to unity for most locations at the surface level, supporting the application of equilibrium SOA partitioning. However, τ eq is prolonged and α eff is lowered over drylands and most regions in the upper troposphere, suggesting that kinetically limited growth would need to be considered for these regions in future large-scale model studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Direct Measurements of Dimethyl Sulfide Oxidation Chemistry: Uncovering the Primary Pathways from Gas Phase to Sulfate Aerosols

We report experimental measurements of dimethyl sulfide oxidation, focusing on a major chemical intermediate, hydroperoxymethyl thioformate (HPMTF). The (photo)chemistry and transport properties of HPMTF, which are critical to accurate modeling of tropospheric sulfur, are not known, partly because current analytical methods are not optimized for elusive chemical intermediates. We investigated dimethyl sulfide oxidation using a custom time-resolved photoionization mass spectrometer, interfaced with pulsed laser photolysis, continuous photolysis, and liquid droplet reactors. We detected and characterized HPMTF (along with other reaction intermediates), quantified its yield, and measured its formation rate coefficient. We also developed a clean, intense, online HPMTF source, suitable for further experimental investigations. Lastly, we measured the mass accommodation coefficient of HPMTF on water droplets – a critical quantity for modeling its atmospheric partitioning between the gas and condensed phases. The experimental platform we developed here enables the exploration of other critical elusive reaction intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Direct measurement of thermal Knudsen forces in rarefied gas environments

At micro- and nanoscales, momentum transfer between surfaces is influenced by various physical mechanisms, including quantum fluctuations, electromagnetic interactions, electric charges, and the dynamics of (rarefied) gases. Under non-isothermal conditions, rarefied gases give rise to thermal Knudsen forces whose magnitudes strongly depend on the gas species and surface characteristics. Knudsen forces are particularly relevant in nanotechnology, optical manipulation, and aerospace systems, where gas rarefaction occurs due to highly confined geometries, sub-micrometer length scales, and reduced particle densities. Despite their significance, predictive modeling of Knudsen forces is limited by a lack of comprehensive experimental data across diverse materials and surface morphologies. Here, in this work, we present a highly sensitive and adaptable measurement platform capable of directly quantifying Knudsen forces using a suspended, interchangeable micro-cantilever within controlled rarefied helium and nitrogen environments. The system integrates optical fiber interferometry to precisely capture out-of-plane displacements at sub-micrometer resolution, driven by Knudsen forces. From the empirical data, we derive a robust correlation linking the magnitudes of Knudsen forces to energy accommodation coefficients, offering deeper insights into the underlying gas–surface interaction mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Investigating the relationship between simulation parameters and flow variables in simulating atmospheric gravity waves for wind energy applications

Wind farms, particularly offshore clusters, are becoming larger than ever before. Besides influencing the surface wind flow and the inflow for downstream wind farms, large wind farms can trigger atmospheric gravity waves in the inversion layer and the free atmosphere aloft. Wind-farm-induced gravity waves can cause adverse pressure gradients upstream of the wind farm, which contribute to the global blockage effect, and can induce favorable pressure gradients above and downstream of the wind farm that enhance wake recovery. Numerical modeling is a powerful means of studying these wind-farm-induced atmospheric gravity waves, but it comes with the challenge of handling spurious reflections of these waves from domain boundaries. Typically, approaches which employ radiation boundary conditions and forcing zones are used to avoid these reflections. However, the simulation setup of these approaches relies heavily on ad hoc processes. For instance, the widely used Rayleigh damping method requires ad hoc tuning to produce a setup that may only produce satisfactory results for a particular case. To provide more systematic guidance on setting up realistic simulations of atmospheric gravity waves, we conduct a large-eddy simulation (LES) study of flow over a 2D hill and through a wind farm canopy that explores the optimum domain size and damping layer setup depending on the fundamental parameters which determine the flow characteristics. In this work, we only consider linearly stratified conditions (i.e., no inversion layer), thereby focusing on internal gravity waves in the free atmosphere and their reflections from the domain boundaries. This type of flow is governed by a single Froude number, which dictates most of the internal wave properties, such as wavelength, amplitude, and direction. This, in turn, will dictate the optimum domain size and Rayleigh damping layer setup. We find the effective horizontal and vertical wavelengths (the representative wavelengths of the entire wave spectrum) to be the appropriate length scales to size the domain and damping layer thickness, and the optimal Rayleigh damping coefficient scales with the Brunt–Väisälä frequency. Considering Froude numbers seen in wind farm applications, we propose recommendations to limit the reflections to less than 10 % of the total upward-propagating wave energy. Typically, damping is done at the top boundary, but given the non-periodic lateral boundary conditions of practical wind farm simulation domains, we find that damping the inflow–outflow boundaries is of equal importance to damping the top boundary. The Brunt–Väisälä frequency-normalized damping coefficient should be between 1 and 10. The damping layer thickness should be at least one effective vertical wavelength; damping layers exceeding 1.5 times the vertical wavelength are found to be unnecessary. The domain length and height should accommodate at least one effective horizontal and vertical wavelength, respectively. Moreover, Rayleigh damping does not damp the waves completely, and the non-damped energy might accumulate over the simulation time.

17 WIND ENERGY↗
DOE OSTI2025

Fuel cells for single-aisle regional aircraft: System configuration, performance and cost

A hydrogen fuel cell propelled electric aircraft can compete with incumbent turbofan technologies for single-aisle regional aircraft by coupling design of stack, air handling, thermal management, propulsion, and airframe to optimize performance. The stack operates at 95°C to facilitate heat rejection during take-off and below 75°C during cruise to extend lifetime and is oversized to satisfy power requirements at end of life. A multi-stage turbocompressor with a compression ratio >10 is selected to reach high stack power density at 11,300-m cruise altitude. The propulsion system is configured to accommodate air handling within the core duct, an inclined heat exchanger in the outer duct to limit the nacelle size, and variable area nozzles to independently control mass flows through the core and bypass ducts. The airframe is modified for maximum lift coefficient and longer balanced field length for dramatically reduced thrust during take-off, and the fuselage is stretched by 20% to store liquid hydrogen (LH 2 ). Modularization of power systems promotes safety in one engine inoperative scenarios and allows reaching specific power metrics for stack, balance-of-plant and fuel cell system (FCS), necessary for acceptable take-off weight. In conclusion, cost parity requires increase in FCS lifetime, LH 2 cost reduction, and improved FCS specific power.

Catalyst durability↗
DOE OSTI2026

Generation of random geological models using multi-randomization for machine learning

Generating high-fidelity geological models is essential for advancing machine learning (ML) methods in automated seismic interpretation. For instance, seismic images paired with corresponding fault labels are foundational for ML-based fault detection from seismic migration sections. While several open-access datasets of random geological models exist, open-source tools specifically designed to produce large volumes of such models for ML applications remain scarce. To address this gap, we present RGM (Random Geological Model), an open-source software package for efficiently generating 2D and 3D synthetic geological models tailored for ML workflows. RGM supports the creation of diverse model components, including medium property distributions (P-/S-wave velocities and density), seismic reflectivity images (i.e., synthetic migration sections), relative geological time, and discrete fault attributes such as probability, dip, strike, rake, and displacement. It also accommodates the creation of complex geological features such as salt bodies and unconformities. The model generation algorithm employs a multi-randomization strategy, yielding an effectively infinite-dimensional model space that encompasses a wide range of geological scenarios and associated seismic features. Furthermore, RGM incorporates a method to generate synthetic elastic migration images using analytical elastic reflection coefficients combined with frequency-dependent scaling. This functionality enables the creation of training datasets for ML models that leverage elastic seismic images. RGM is implemented in modern object-oriented Fortran, allowing users to flexibly control statistical parameters governing model variability. We demonstrate the capability, performance, and geological realism of the package through comprehensive 2D and 3D examples.

58 GEOSCIENCES↗
DOE OSTI2026

Adaptive Control for Load-Following of Boiling Water Reactors Part I: Linear Systems and Fully-Observable Dynamics

Automation control is a key strategy to improve the economic competitiveness of nuclear power plants. Not only does it help reduce operational costs, but it also extends the value proposition of these plants to nontraditional markets, including unattended operations in remote villages and space. However, the dynamics of the operating environments of nuclear reactors are subject to changes over time, and there are no widely adopted methods to ensure that the automation strategy will remain effective over the extended durations required for these applications. Adaptive control is a discipline that offers the possibility to accommodate such changes online. However, it relies on mathematical assumptions that must be respected to ensure robustness and reliability. In this work, we derive an adaptive control formulation for linear systems in which all states are observable and apply it to an instance of load-follow operation for Boiling Water Reactors. We assumed uncertainty in two factors: the temperature coefficient and the control rod worth, both of which are affected over time by the evolution of the nuclear reactor core environment. With an arbitrary penalty factor of 5, we found that the mean absolute and integral time absolute errors can be reduced by more than 90%, underscoring the strength of adaptive control. To extend the application to more challenges, different uncertainties and load-follow trajectories, as well as new formulations that include non-linearity and partial observability, are currently being developed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Rapid Solidification Effects in Additively Manufactured Si and SiGe Compositions

SiGe alloys have a proven track record as robust high-temperature thermoelectric materials, powering NASA missions like SNAP-10A, LES-9, and Voyager 1 and 2. Enhancing thermoelectric efficiency hinges on minimizing thermal conductivity while preserving electrical conductivity. Rapid solidification via LPBF can generate microstructural features such as subgrain cellular boundaries and twinning, which may help reduce thermal conductivity while preserving semiconducting behavior. Here, this study investigates the potential of laser powder bed fusion (LPBF) additive manufacturing to fabricate nanostructured Si and SiGe thermoelectric materials. High cooling rates (10 5 to 10 7 K/s) rates inherent to the LPBF process are conducive to forming such nanostructures. Moreover, this fabrication technique could also be suitable for fabricating complex geometries needed to achieve improved device level performance. Process mapping of commercial Si powder with irregular morphology was first performed to understand the LPBF processing behavior of this semiconductor material. Subsequent studies included in-house synthesized B-doped (p-type) Si7 8 Ge 22 spherical powder that was produced via ultrasonic atomization. Scan strategies involved multiple laser exposures to mitigate solidification cracking: a high density of >98% was achieved, but solidification cracking could not be fully eliminated. Subsequently, a high electrical resistivity (i.e., low conductivity) was observed, but the measured Seebeck coefficient, ∼230 μV/K @ 500 °C, proved that good semiconductor material was being fabricated. A subgrain cellular structure (5–10 μm) was observed as defined by Ge segregation to the intercellular boundaries. The remelting strategies helped lower the cooling rates in processing SiGe, but this still resulted in high residual stresses, which induced a remarkably high density of twins (78–95%) to accommodate the deformation. This unique grain structure offers an avenue for phonon scattering and potential improvements in thermoelectric performance.

figure of merit↗
DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗
DOE OSTI2024

Component-based SHGC determination of BIPV glazing for product comparison

Building-integrated photovoltaic (BIPV) systems are intrinsically designed to generate electricity and to provide at least one building-related function. When BIPV modules act as glazing products in windows, skylights or curtain walls, their ability to control the transmission of solar energy into the building must be characterised by a Solar Heat Gain Coefficient (SHGC) or g value (also known as Total Solar Energy Transmittance – TSET – or “solar factor”). For the comparison of BIPV glazing products consisting of one PV laminate and possibly further, conventional glazing layers separated by gas-filled cavities, the procedures documented in international standards for architectural glazing (e.g. ISO 9050 and EN 410) form a suitable starting point. Easily implemented modifications to these procedures are proposed to take both optical inhomogeneity (if relevant) and extraction of electricity from BIPV glazing units into account. Geometrically complex glazing and shading devices, and light-scattering glazing layers, are outside the scope of the proposed methodology; SHGC determination for obliquely incident solar radiation is also excluded. For these cases, the experimental calorimetric approach documented in [ISO 19467:2017; ISO 19467-2:2021] is recommended. The paper also presents results and conclusions from an implementation exercise and sensitivity study carried out by participants of the IEA-PVPS Task 15 on BIPV. The cell coverage ratio in the PV laminate, the thermal resistance offered by the glazing configuration, the choice of boundary conditions and the effect of extracting electricity were all identified as parameters which significantly affect the SHGC value determined for a given type of BIPV glazing. A practicable approach to accommodate the great variety of dimensions typical for BIPV glazing is also proposed. These findings should pave the way for modifying the existing component-based standards for architectural glazing to take the specific features of BIPV glazing into account.

14 SOLAR ENERGY↗
DOE OSTI2025

Structural stability, elemental ordering, and transport properties of layered ScTaN 2

Ternary transition metal (TM) nitrides have gained significant attention in thin film research due to their promising properties for a broad range of applications. Particularly, some of the ternary TM nitrides have been predicted to adopt layered structures that make them interesting for thermoelectric conversion and quantum materials applications. Unfortunately, synthesis of TM ternary nitride films by physical vapor deposition often favors disordered 3D structures rather than the predicted 2D-like layered structure. In this study, we investigate the structural interplay in the Sc-Ta-N ternary system using a combinatorial approach. Combinatorial libraries S⁢c 𝑥 ⁢T⁢a 1−𝑥 ⁢N are synthesized following a two-step method: First, deposit film precursors by cosputtering and then process the resulting 3D-structured samples with rapid thermal annealing. Synchrotron grazing-incidence wide-angle x-ray scattering on films annealed at 1200 ⁢°⁢C for 20 min leads to the nucleation of ScTaN 2 layered structure (𝑃⁢6 3 /𝑚⁢𝑚⁢𝑐) near stoichiometry. We find that the layered structure can accommodate large off-stoichiometry in the Ta-rich region (𝑥 < 0.5), facilitated by the alloying with quasi-isostructural Ta 5 ⁢N 6 compound that exists on a composition tie line at 𝑥 = 0. While focusing on ScTaN 2 , we estimate the long-range order parameter in near-stoichiometric films to be 0.86, corresponding to a fraction of Sc/Ta antisites of 7%. Transport measurements on ScTaN 2 reveal a nearly temperature-independent high carrier density (10 21 c⁢m −3 ), suggesting a heavily doped semiconductor or semimetallic character, consistent with a small positive Seebeck coefficient of +19 µV/K. The carrier mobility at 2 K is relatively small (9.5c⁢m 2 V −1 s −1 ) and the residual-resistivity ratio is minor, suggesting that electrical conduction is dominated by defects or disorder. Measured magnetoresistance suggests possible weak antilocalization at 2 K. This paper highlights the interplay between ScTaN 2 and Ta 5 ⁢N 6 crystal structures in stabilizing layered materials, emphasizes the importance of cation order/disorder for potential tunable alloys, and suggests that ScTaN 2 is a promising platform for exploring electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tuning Perovskites’ Hydration-Induced Chemical Expansion with Octahedral Tilt Angles

Hydration-induced strains in proton-conducting oxides compromise chemo-mechanical stability when these materials are applied in protonic ceramic electrochemical cells. Here, to develop design principles for zero-strain materials, we systematically studied the hydration coefficients of chemical expansion (CCEs) in perovskite (Sr, Ba)(Ce, Zr, Y)O 3–x solid solutions with in situ dilatometry and thermogravimetric analysis in the range of 430–630 °C. By including and decoupling a wide range of tolerance factors and lattice parameters, we were able to identify a minimum in hydration CCEs (0–0.02) at intermediate tolerance factor values (t ≈ 0.95). Conversely, despite expectations of lower CCEs in larger unit cells, no general trend in CCE versus lattice parameter was found, and opposite trends could be seen for Sr(Ce, Zr, Y)O 3–x versus Ba(Ce, Zr, Y)O 3–x separately. In situ neutron diffraction (ND) enabled atomistic insight. Upon decreasing t, chemical strain anisotropy increased, but this trend did not match the U-shaped dependence of macroscopic CCEs on t. Instead, perovskites with intermediate t, hosting intermediate octahedral tilt angles in the nominally dry state, underwent the largest change in the B–O–B angles during hydration. Accommodating hydration through decreasing B–O–B angles is beneficial because it does not result in large lattice parameter changes. We propose an intermediate tolerance factor as a simple structural descriptor to enable near-zero hydration strains in proton-conducting perovskites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Free-Standing Boron-Doped Diamond Grid Electrode for Fundamental Spectroelectrochemistry

Spectroelectrochemistry (SEC) is a powerful technique that enables a variety of redox properties to be studied, including formal potential (E o ), thermodynamic values (ΔG, ΔH, ΔS), diffusion coefficient (D), electron transfer stoichiometry (n), and others. SEC requires an electrode which light can pass through while maintaining sufficient electrical conductivity. This has been traditionally composed of metal or metal oxide films atop transparent substrates like glass, quartz, or metallic mesh. Robust electrode materials like boron-doped diamond (BDD) could help expand the environments in which SEC can be performed, but most designs are limited to thin films (~100–200 nm) on transparent substrates less resilient than free-standing BDD. Here, this work presents a free-standing BDD grid electrode (G-BDD) for fundamental SEC measurements, using the well-characterized Fe(CN) 6 3–/4– redox couple as proof-of-concept. With a combination of cyclic voltammetry (CV), thin-layer SEC, and chronoabsorptometry, several of the redox properties mentioned above were calculated and compared. For E o' , n, and D, similar results were obtained when comparing the CV [E o' = +0.279 (±0.002) V vs Ag/AgCl; n = 0.97; D = 4.1 × 10 –6 cm 2 ·s –1 ] and SEC [E o' = +0.278 (±0.001) V vs Ag/AgCl; n = 0.91; D = 5.2 × 10 –6 cm 2 ·s –1 ] techniques. Both values align with what has been previously reported. To calculate D from the SEC data, modification of the classical equation used in chronoabsorptometry was required to accommodate the G-BDD electrode geometry. Overall, this work expands on the applicability of SEC techniques and BDD as a versatile electrode material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K 1 , S 1 , and P 1 ) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S 1 ) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Lobo, Matthew↗