PREreview del Dual-purpose architected materials: Optimizing graded BCC lattices for crashworthiness and heat dissipation

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Recommended for submission to Tier2 (Graduate journals) — Acceptable with minor revisions. Sound multiphysics simulation and surrogate optimization with clear trade-off analysis; however, experimental validation is absent, some assumptions are strong (e.g., contact resistance = 0, rate-independent plasticity), robustness across operating conditions is limited, and a few reference/claim issues require correction. These gaps place it below Tier3 expectations.

The Citation Propensity Index for this paper is 0.656

The expected 2 year citation count for this paper is 13

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Dual-purpose architected materials: Optimizing graded BCC lattices for crashworthiness and heat dissipation by Jaswanth V. Gurudev and Ratna Kumar Annabattula presents a two-zone/three-planar functional grading strategy for Body-Centered Cubic (BCC) lattices and a multi-objective surrogate-assisted optimization (TPS-RBF + goal programming) to jointly improve impact energy absorption (SEA, peak stress) and thermal performance (Nusselt number, pressure drop). Using Abaqus/Explicit impact simulations and Ansys Fluent forced-convection CFD, the study identifies two Pareto-optimal designs (O1, O2), with O2 delivering substantially higher SEA and reduced peak stress and pressure drop relative to a uniform baseline, while maintaining comparable heat transfer.

Category:Score:Reason

Abstract: 9: Clear, self-contained summary of objectives (dual mechanical–thermal optimization), methods (graded BCC, TPS-RBF surrogates, goal programming), metrics (SEA, σp, Nu, ΔP), and headline findings (O1/O2 trade-offs).

Recency: 9: References include multiple 2022–2025 sources across metamaterials, thermal management, and optimization; foundational works are also cited.

Scope: 9: Content matches title and keywords: graded BCC lattices for crashworthiness and heat dissipation via multiphysics optimization; the methodology and analysis reflect that dual scope.

Relevance: 8: Addresses a practical and underexplored multiphysics design gap; background is concise with minimal digressions. Novelty is incremental but useful in combining grading strategy and goal programming.

Factual Errors: 6: Two notable issues: (1) The assertion Power ∝ (ΔP)^3 is not generally valid for fan/pump systems (fan/pump affinity laws imply P ∝ Q^3 and ΔP ∝ Q^2, giving P ∝ ΔP^(3/2) under typical conditions); (2) wording states the chip–lattice interface is thermally coupled to 'prevent heat loss ... (zero thermal resistance)', which is contradictory—zero interfacial resistance enables, not prevents, heat transfer. Minor parameter inconsistencies and incomplete Ref. [47] metadata also require correction.

Language: 7: Technical writing is generally clear and objective; a few typos/wording issues (e.g., ‘effeciency’, occasional awkward phrasing) and non-scholarly references in motivation diminish polish.

Formatting: 7: Overall consistent; equations and symbols are mostly clear. A few notational repetitions (e.g., primes) and minor cross-referencing/citation formatting issues (e.g., [47] details) should be standardized.

Suggestions: 8: The framework is useful; to strengthen impact: (i) validate with experiments for both domains; (ii) add sensitivity to inlet velocity/heat flux and impact speed; (iii) compare turbulence models/wall treatment; (iv) include contact resistance at chip–lattice interface; (v) explore strain-rate and hardening; (vi) publish parametric CAD/meshes and scripts for reuse.

Problems: 8: Addresses the gap of single-objective optimization by demonstrating explicit multiphysics trade-offs and Pareto reasoning; notes that high Nu does not guarantee low chip temperature, highlighting practical significance beyond headline metrics.

Assumptions: 6: Strong simplifications: incompressible/constant-property air, no radiation/buoyancy, zero interfacial resistance, steady-state CFD, single turbulence model, and rate-independent perfectly plastic metal. Reasonable for a first pass but require validation/sensitivity analysis.

Consistency: 8: Surrogates align with simulations (small GP vs. simulation errors), and observed trade-offs are physically plausible (e.g., thin upper struts reduce ΔP and σp).

Robustness: 7: Mesh convergence is shown for both domains and LOOCV is used for surrogates; however, only one impact speed, one inlet velocity, one turbulence model, and one material law are considered. Broader parametric variations would increase robustness.

Logic: 8: Conclusions follow from data: O1’s high Nu yet worse chip temperature is logically explained via flow blockage; O2’s graded progression supports higher SEA and lower σp with acceptable thermal behavior.

Statistical Analysis: 7: LOOCV, RMSE/MAE for surrogates and mesh convergence are reported; however, no uncertainty quantification (confidence intervals) for key metrics and surrogate predictions is provided. Considering the simulation focus, this is adequate but could be stronger.

Controls: N/A: Not an experimental study without models/simulations; control arms are not applicable.

Corrections: 7: Mass-normalized SEA and density constraints reduce confounding; still, thermal comparisons should account for contact resistance and potential temperature-dependent fluid properties.

Range: 8: Design variable ranges (0.5–2.58 mm) are well-defined with manufacturing and density constraints; Latin Hypercube and corner sampling plus feasible-region filtering are appropriate, though expanding sample count would help nonlinear regions.

Collinearity: 7: Only two design variables (d1, d2) with monotonic constraints and implied d3 reduce degeneracy. Some correlation between d1 and d2 effects may persist; explicit sensitivity/variance decomposition would clarify.

Dimensional Analysis: 8: Use of dimensionless groups (Nu, Cf, β) is appropriate; derived area-porosity expression is dimensionless and consistent. Check constants in SST transport equations for standard values to avoid confusion.

Ethical Standards: informational: No human/animal data; no apparent ethical concerns. Recommend adding a statement on data/code availability and computational reproducibility.

Conflict Of Interest: informational: No conflicts disclosed; include an explicit COI statement.

Normalization: informational: Category not applicable to an experimental dataset without modeling; the study uses modeling/simulation and mass-normalized SEA appropriately.

Experimental Design: 7: Simulation setups are well-described (BCs, meshing, convergence). Improvements: (i) wall treatment/y+ reporting for SST; (ii) turbulence-model sensitivity; (iii) inlet/outlet domain-size checks; (iv) contact resistance modeling; (v) rate-dependent plasticity and hardening; (vi) experimental validation for key Pareto points.

Idea Incubator: informational: Analogies: 1) Economics (progressive taxation): allocating thicker struts near the heat source is akin to taxing regions with higher ‘load’ to stabilize the system; the marginal change in diameter maps to marginal utility in resource allocation under constraints. 2) Ecology (edge effects): thin upper struts promote mixing akin to biodiversity at forest edges; gradients in structure create microclimates that influence flow and heat-transfer ‘species’ interactions. 3) Physics (impedance matching): grading struts resembles impedance matching between high-thermal-resistance chip and low-resistance airflow to minimize reflection (flow blockage) and maximize transmission (heat removal). 4) Control systems (gain scheduling): varying strut diameters across height is like scheduling controller gains across operating regimes, stabilizing responses (peak stress) while maintaining performance (Nu). 5) Information theory (rate–distortion): optimizing β and SEA is like trading off rate and distortion; denser regions encode more ‘signal’ (heat/force conduction) at the cost of ‘bandwidth’ (pressure drop), and the Pareto set traces feasible compressions. 6) Network science (core–periphery): thick basal struts form a robust core for heat conduction and load transmission; thin periphery enhances flow connectivity, akin to resilient transport networks. 7) Chemical engineering (packed-bed reactors): pressure drop and reaction/heat-transfer effectiveness trade off with particle size gradients; the graded lattice mirrors particle size grading to balance ΔP and conversion (Nu).

Improve Citability: informational: To maximize reuse: (i) release parametric CAD and meshing templates (with exact d0–d3 values) under an open license; (ii) publish DOE seeds, LHS/GPD selection code, and full simulation input decks (Abaqus .inp, Fluent case/data); (iii) provide surrogate training data and fitted TPS-RBF weights/matrices for direct replication; (iv) document goal-programming scripts and penalty configurations; (v) add a validation plan/protocol (measurement points, sensors, flow loop specs) so others can revalidate; (vi) include uncertainty budgets (mesh, turbulence model, material model) and a checklist; (vii) assign DOIs to datasets/models.

Falsifiability: informational: Primary claims: (1) Strategic density gradation in BCC lattices can concurrently reduce ΔP and σp while improving SEA and maintaining or modestly improving Nu; (2) O2-like grading yields superior overall trade-offs vs. uniform BCC. Potential falsifiers: experimental tests showing (a) no reduction in σp or SEA improvement for O2 vs. ground at 15 m/s impact; (b) ΔP not reduced and/or chip temperature not improved or worsened compared to baseline under the stated jet-impingement conditions; (c) surrogate/goal-programming predictions significantly diverge (>10–15%) from measurements across multiple (d1,d2) points; (d) sensitivity tests demonstrating reversal of predicted trends under modest variations of inlet velocity or heat flux.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.